{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c47e9409-c256-4fc4-b2ac-a000de24c565",
   "metadata": {},
   "source": [
    "# 量化交易基础"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "911acffe-27cd-402b-9432-565b3002dbe1",
   "metadata": {},
   "source": [
    "## 1 聚宽量化平台API使用\n",
    "安装`pip install jqdatasdk`\n",
    "\n",
    "导入`import jqdatasdk`\n",
    "\n",
    "升级JQData：`pip install -U jqdatasdk`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fd4959-91c0-4974-b131-725762aa43fc",
   "metadata": {},
   "source": [
    "### 1.1 初始化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a52b15b-5541-4270-81c6-7a8be727f23d",
   "metadata": {},
   "source": [
    "* 读取用户名和密码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8321e96c-5565-4b71-87de-a2e28ca3ab2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在当前文件夹下新建user.txt文件，并输入用户名和密码保存，注重换行\n",
    "with open('./user.txt','r') as f:\n",
    "    username = f.readline().strip()\n",
    "    password = f.readline().strip()\n",
    "# username, password # 查看用户名和密码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b03bd2f-8e13-4e66-b4a6-25246100e811",
   "metadata": {},
   "source": [
    "* 登录聚宽帐号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "059a82ce-653e-405e-9b8f-3df25fdb9564",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jqdatasdk\n",
    " \n",
    "# 登录帐号\n",
    "jqdatasdk.auth(username=username, password=password)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63d6b85-7892-4e70-b6e7-e9ce1b839ab2",
   "metadata": {},
   "source": [
    "* 导入所需模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f294ccbd-4c64-4b62-8498-489aab30b3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 运行时配置参数\n",
    "# rcParames：runtime configuration Parameters\n",
    "\n",
    "# 如果浏览器不显示图片，就需要加上这句话\n",
    "%matplotlib inline\n",
    "# 让图片中可以显示中文\n",
    "plt.rcParams['font.sans-serif'] = 'Simhei'\n",
    "# 让图片可以显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 支持svg矢量图\n",
    "%config Inlinebakend.figuer_format = 'svg'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590aaad1-fe47-4938-98f0-b5a2d0b869e6",
   "metadata": {},
   "source": [
    "* 查询账户状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7d5835ff-0f40-4801-965f-5903ebff2ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mob': '15500802551',\n",
       " 'query_count_limit': 1000000,\n",
       " 'license': 1,\n",
       " 'expire_time': '2025-09-30 00:00:00',\n",
       " 'date_range_start': '2024-03-21 00:00:00',\n",
       " 'date_range_end': '2025-03-28 00:00:00'}"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jqdatasdk.get_account_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5808043f-19c8-404e-8b2a-76f39e6fcb96",
   "metadata": {},
   "source": [
    "* 查询剩余流量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c67c6f50-c0cd-4916-953f-7b4c53c301af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total': 1000000, 'spare': 993474}"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jqdatasdk.get_query_count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6fb7de-e1d8-48ad-b8f2-7b5b7aa1377b",
   "metadata": {},
   "source": [
    "### 1.2 基础操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34554bdd-5b4f-4898-9f7a-316108d34e59",
   "metadata": {},
   "source": [
    "#### 1.2.1 获取所有股票、基金、指数、期货信息\n",
    "* `get_all_securities()`\n",
    "  \n",
    ":param types list: 用来过滤securities的类型, list元素可选: ‘stock’, ‘fund’, ‘index’, ‘futures’, ‘etf’, ‘lof’, ‘fja’, ‘fjb’. types为空时返回所有股票, 不包括基金,指数和期货\n",
    "\n",
    ":param date 日期, 一个字符串或者 datetime.datetime/datetime.date 对象, 用于获取某日期还在上市的股票信息. 默认值为 None, 表示获取所有日期的股票信息\n",
    "\n",
    ":return pandas.DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1dcead91-3c26-431e-939a-8862687227e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>display_name</th>\n",
       "      <th>name</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001.XSHE</th>\n",
       "      <td>平安银行</td>\n",
       "      <td>PAYH</td>\n",
       "      <td>1991-04-03</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.XSHE</th>\n",
       "      <td>万科A</td>\n",
       "      <td>WKA</td>\n",
       "      <td>1991-01-29</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.XSHE</th>\n",
       "      <td>*ST国华</td>\n",
       "      <td>*STGH</td>\n",
       "      <td>1990-12-01</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.XSHE</th>\n",
       "      <td>ST星源</td>\n",
       "      <td>STXY</td>\n",
       "      <td>1990-12-10</td>\n",
       "      <td>2024-04-25</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.XSHE</th>\n",
       "      <td>深振业A</td>\n",
       "      <td>SZYA</td>\n",
       "      <td>1992-04-27</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688799.XSHG</th>\n",
       "      <td>华纳药厂</td>\n",
       "      <td>HNYC</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688800.XSHG</th>\n",
       "      <td>瑞可达</td>\n",
       "      <td>RKD</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688819.XSHG</th>\n",
       "      <td>天能股份</td>\n",
       "      <td>TNGF</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688981.XSHG</th>\n",
       "      <td>中芯国际</td>\n",
       "      <td>ZXGJ</td>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689009.XSHG</th>\n",
       "      <td>九号公司</td>\n",
       "      <td>JHGS</td>\n",
       "      <td>2020-10-29</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5431 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            display_name   name start_date   end_date   type\n",
       "000001.XSHE         平安银行   PAYH 1991-04-03 2200-01-01  stock\n",
       "000002.XSHE          万科A    WKA 1991-01-29 2200-01-01  stock\n",
       "000004.XSHE        *ST国华  *STGH 1990-12-01 2200-01-01  stock\n",
       "000005.XSHE         ST星源   STXY 1990-12-10 2024-04-25  stock\n",
       "000006.XSHE         深振业A   SZYA 1992-04-27 2200-01-01  stock\n",
       "...                  ...    ...        ...        ...    ...\n",
       "688799.XSHG         华纳药厂   HNYC 2021-07-13 2200-01-01  stock\n",
       "688800.XSHG          瑞可达    RKD 2021-07-22 2200-01-01  stock\n",
       "688819.XSHG         天能股份   TNGF 2021-01-18 2200-01-01  stock\n",
       "688981.XSHG         中芯国际   ZXGJ 2020-07-16 2200-01-01  stock\n",
       "689009.XSHG         九号公司   JHGS 2020-10-29 2200-01-01  stock\n",
       "\n",
       "[5431 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jqdatasdk.get_all_securities()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a5eeb7-ef26-4293-9182-beda691119ee",
   "metadata": {},
   "source": [
    "#### 1.2.2 获取行情数据\n",
    "* `get_price()`\n",
    "* param\n",
    "\n",
    "    security, 一支证券代码或者一个证券代码的list\n",
    "  \n",
    "    start_date=None,\n",
    "  \n",
    "    end_date=None,\n",
    "  \n",
    "    frequency='daily',\n",
    "  \n",
    "    fields=None,  默认是None(表示['open', 'close', 'high', 'low', 'volume', 'money']这几个标准字段), 支持以下属性 ['open', 'close', 'low', 'high', 'volume', 'money', 'factor', 'high_limit', 'low_limit', 'avg', 'pre_close', 'paused']\n",
    "  \n",
    "    skip_paused=False,\n",
    "  \n",
    "    fq='pre',\n",
    "  \n",
    "    count=None,\n",
    "  \n",
    "    panel=True,\n",
    "  \n",
    "    fill_paused=True,\n",
    "  \n",
    "    round=True,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a5afe792-cd21-42f5-ae07-277af71429c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "security = '000001.XSHE'# 一支证券代码或者一个证券代码的list\n",
    "df = jqdatasdk.get_price(security, start_date='2025-1-1',end_date='2025-3-28')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "dfafea17-4d4b-4fb3-97d5-f052f3376b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-01-02</th>\n",
       "      <td>11.37</td>\n",
       "      <td>11.08</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.04</td>\n",
       "      <td>187660786.0</td>\n",
       "      <td>2.102923e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-03</th>\n",
       "      <td>11.09</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.01</td>\n",
       "      <td>119085841.0</td>\n",
       "      <td>1.320521e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-06</th>\n",
       "      <td>11.03</td>\n",
       "      <td>11.09</td>\n",
       "      <td>11.13</td>\n",
       "      <td>10.88</td>\n",
       "      <td>111954788.0</td>\n",
       "      <td>1.234306e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-07</th>\n",
       "      <td>11.07</td>\n",
       "      <td>11.16</td>\n",
       "      <td>11.18</td>\n",
       "      <td>11.02</td>\n",
       "      <td>77129461.0</td>\n",
       "      <td>8.583290e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-08</th>\n",
       "      <td>11.15</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.05</td>\n",
       "      <td>109567225.0</td>\n",
       "      <td>1.223599e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open  close   high    low       volume         money\n",
       "2025-01-02  11.37  11.08  11.41  11.04  187660786.0  2.102923e+09\n",
       "2025-01-03  11.09  11.03  11.19  11.01  119085841.0  1.320521e+09\n",
       "2025-01-06  11.03  11.09  11.13  10.88  111954788.0  1.234306e+09\n",
       "2025-01-07  11.07  11.16  11.18  11.02   77129461.0  8.583290e+08\n",
       "2025-01-08  11.15  11.15  11.28  11.05  109567225.0  1.223599e+09"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "df.head()\n",
    "# df.info()\n",
    "# df.to_csv('./000001(20250101-20250328).csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf46724-8ee4-4c65-8da4-7d9b05fc44e9",
   "metadata": {},
   "source": [
    "## 2 股票常用指标"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05959a32-e1d7-4c1a-865f-8b21b6617ac6",
   "metadata": {},
   "source": [
    "### 2.1 极差\n",
    "股价近期最高价的最大值和最小值的差值\n",
    "* np.ptp()函数可以直接得到极差\n",
    "* 越高说明波动越明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dfbc7897-9bfb-473b-8897-57d8a6b91ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-01-02</th>\n",
       "      <td>11.37</td>\n",
       "      <td>11.08</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.04</td>\n",
       "      <td>187660786.0</td>\n",
       "      <td>2.102923e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-03</th>\n",
       "      <td>11.09</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.01</td>\n",
       "      <td>119085841.0</td>\n",
       "      <td>1.320521e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-06</th>\n",
       "      <td>11.03</td>\n",
       "      <td>11.09</td>\n",
       "      <td>11.13</td>\n",
       "      <td>10.88</td>\n",
       "      <td>111954788.0</td>\n",
       "      <td>1.234306e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-07</th>\n",
       "      <td>11.07</td>\n",
       "      <td>11.16</td>\n",
       "      <td>11.18</td>\n",
       "      <td>11.02</td>\n",
       "      <td>77129461.0</td>\n",
       "      <td>8.583290e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-08</th>\n",
       "      <td>11.15</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.05</td>\n",
       "      <td>109567225.0</td>\n",
       "      <td>1.223599e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open  close   high    low       volume         money\n",
       "2025-01-02  11.37  11.08  11.41  11.04  187660786.0  2.102923e+09\n",
       "2025-01-03  11.09  11.03  11.19  11.01  119085841.0  1.320521e+09\n",
       "2025-01-06  11.03  11.09  11.13  10.88  111954788.0  1.234306e+09\n",
       "2025-01-07  11.07  11.16  11.18  11.02   77129461.0  8.583290e+08\n",
       "2025-01-08  11.15  11.15  11.28  11.05  109567225.0  1.223599e+09"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 例：以numpy实现\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "file_name = './000001(20250101-20250328).csv'\n",
    "df = pd.read_csv(file_name,index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1085ea9c-2e78-40b9-ba04-5c2ee88db34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# end_price ,volumn = np.loadtxt(\n",
    "#     fname=file_name,\n",
    "#     delimiter=',',\n",
    "#     usecols=(1,4),\n",
    "#     unpack=True, # 是否解包\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80801995-67e4-477d-ba61-2c2881fa397b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_price: 11.64\n",
      "min_price: 10.74\n"
     ]
    }
   ],
   "source": [
    "# 计算最大值和最小值\n",
    "high_price = df['high'].values\n",
    "low_price = df['low'].values\n",
    "# print(high_price)\n",
    "# print(low_price)\n",
    "print(f'max_price: {np.max(high_price)}')\n",
    "print(f'min_price: {np.min(low_price)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6516bcfa-e413-406e-94ce-b43ba736f363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max - min of high price: 0.7200000000000006\n",
      "max - min of low price: 0.7200000000000006\n"
     ]
    }
   ],
   "source": [
    "# 计算极差\n",
    "print(f'max - min of high price: {np.ptp(high_price)}')\n",
    "print(f'max - min of low price: {np.ptp(low_price)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7fa141e-a142-49a1-8533-1a269d86b5b1",
   "metadata": {},
   "source": [
    "### 2.2 成交量加权平均价格\n",
    "英文名VWAP(Volume-Weighted Average Price,成交量加权平均价格)是一个非常重要的经济学量，代表着金融资产的“平均价格”\n",
    "* 若是计算某一证券在某交易日的VWAP，将当日成交总值除以总成交量即可\n",
    "* VWAP可作为交易定价的一种方法，亦可作为衡量机构投资者或交易商的交易表现的尺度"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac7d7e1-3e69-4e04-8bd3-5718a5455d41",
   "metadata": {},
   "source": [
    "* 计算成交量加权平均价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "235bc5eb-f6a2-45db-998f-429298332502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_price = 11.149464285714284\n",
      "VWAP = 11.16728408559643\n"
     ]
    }
   ],
   "source": [
    "# 计算成交量加权平均价格\n",
    "# 设计两个重要指标，收盘价和成交量\n",
    "end_price = df['close'].values # 收盘价\n",
    "volumn = df['volume'].values # 成交量\n",
    "\n",
    "avg_price = np.average(end_price) # 平均价格\n",
    "VWAP = np.average(end_price, weights=volumn) # 成交量加权平均价格\n",
    "\n",
    "print(f'avg_price = {avg_price}')\n",
    "print(f'VWAP = {VWAP}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9195f7-9ee7-4660-800f-c205c64476b3",
   "metadata": {},
   "source": [
    "* 计算中位数\n",
    "  * 又称中点数，中值。中位数是按顺序排列的一组数据中居于中间位置的数，即在这组数据中，有一半的数据比他大，有一半的数据比他小\n",
    "  * 计算收盘价的中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22629ca4-4b14-4c0e-9de1-a2f27c680dfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median = 11.135000000000002\n"
     ]
    }
   ],
   "source": [
    "end_price = df['close'].values # 收盘价\n",
    "print(f'median = {np.median(end_price)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbf488a-7788-4f07-adb6-dc540641f474",
   "metadata": {},
   "source": [
    "* 计算方差\n",
    "  * 方差在金融与投资分析中用于衡量资产收益波动与金融资产的风险高低，方差越大风险越高，方差越小风险越低\n",
    "  * 计算收盘价的方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86e2227b-19f3-4f53-80bb-91c4f9cfc5a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var = 0.024355070153061226\n"
     ]
    }
   ],
   "source": [
    "var = np.var(end_price)\n",
    "print(f'var = {var}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b89d81c-5a8d-4028-abf3-926c08737d78",
   "metadata": {},
   "source": [
    "### 2.3 收益率\n",
    "* 简单收益率，相邻两个价格之间的变化率\n",
    "* 对数收益率,指所有价格取对数后两两之间的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bed1d5cd-794b-4d10-8cfb-874abc0617c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log_return =  [-0.00452285  0.00542497  0.00629216 -0.00089646 -0.00900907 -0.00817815\n",
      " -0.00916597  0.01553252  0.00902533  0.00805373 -0.01075279 -0.00270636\n",
      " -0.00725298 -0.02208001  0.02116968  0.00181984  0.01085002 -0.00903349\n",
      " -0.00090785  0.00181488  0.00452285 -0.00090293  0.          0.00720075\n",
      "  0.00447428  0.01945243  0.00262353 -0.00877199 -0.00353045 -0.00176991\n",
      " -0.00443853 -0.01073356  0.00448632  0.00891271 -0.00801786 -0.00179051\n",
      "  0.          0.01335133 -0.00265604  0.00353983 -0.00709223  0.00177778\n",
      "  0.02022047 -0.0008707   0.0112604  -0.0404273  -0.00089726  0.00268938\n",
      " -0.00268938 -0.00630349 -0.00361991  0.00452285 -0.00452285  0.00090621\n",
      " -0.00272109]\n"
     ]
    }
   ],
   "source": [
    "end_price = df['close'].values # 收盘价\n",
    "# 对数收益率\n",
    "log_return = np.diff(np.log(end_price))\n",
    "print('log_return = ',log_return)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a1ffae-9842-4bd9-a937-a24fdc24d3a7",
   "metadata": {},
   "source": [
    "### 2.4 波动率\n",
    "波动率是对价格变动的一种衡量\n",
    "* 越高说明波动越明显"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307b0b15-efd9-4514-9e41-f7d7f286e93b",
   "metadata": {},
   "source": [
    "#### 2.4.1 年波动率\n",
    "对数波动率的标准差除以其平均值，再除以交易日倒数的平方根，通常交易日取250天。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72f48479-9ad1-479b-b6c9-eb3f65c5bfb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annual_volatility =  -5393927.424667589\n"
     ]
    }
   ],
   "source": [
    "# 年度收益率\n",
    "annual_volatility = log_return.std() / log_return.mean() * np.square(250)\n",
    "print('annual_volatility = ',annual_volatility)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8203cf04-12a0-4851-9386-5def6444b6cc",
   "metadata": {},
   "source": [
    "#### 2.4.2 月波动率\n",
    "对数波动率的标准差除以其平均值，再除以交易日倒数的平方根，通常交易日取12月。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "918f5bfd-fd45-4937-9d25-e6931ded3796",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mounth_volatility =  -12427.608786434126\n"
     ]
    }
   ],
   "source": [
    "mounth_volatility = log_return.std() / log_return.mean() * np.square(12)\n",
    "print('mounth_volatility = ',mounth_volatility)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd896386-686a-47eb-a8b9-9bfabdaaf740",
   "metadata": {},
   "source": [
    "### 2.5 股价均线"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d41e3d-97f7-4887-a4f2-c386cae581d2",
   "metadata": {},
   "source": [
    "> **卷积**\n",
    "> * 卷积可用于描述过去作用对当前的影响\n",
    "> * 卷积是时空响应的叠加，可用作计算“滑动平均”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae2488b-2711-4c19-8c7c-493c70e620ed",
   "metadata": {},
   "source": [
    "#### 2.5.1 简单移动均线\n",
    "一般用于分析时间序列上的股价趋势\n",
    "\n",
    "计算股价与等权重的指示函数的卷积"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa59342-f373-4f52-85d2-1204b5305fc8",
   "metadata": {},
   "source": [
    "```mermaid\n",
    "graph LR;\n",
    "    A(生成权重) --> B(卷积计算);\n",
    "    B --> C(均线可视化);\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6ecf412a-4f6a-4581-a643-8c527a3f3ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.102, 11.096, 11.082, 11.036, 11.01 , 11.006, 11.04 , 11.068,\n",
       "       11.11 , 11.102, 11.026, 10.978, 10.958, 10.968, 10.974, 11.026,\n",
       "       11.036, 11.052, 11.042, 11.052, 11.08 , 11.114, 11.182, 11.258,\n",
       "       11.314, 11.346, 11.364, 11.328, 11.262, 11.226, 11.218, 11.196,\n",
       "       11.18 , 11.188, 11.216, 11.218, 11.246, 11.262, 11.282, 11.318,\n",
       "       11.358, 11.416, 11.398, 11.374, 11.31 , 11.242, 11.134, 11.11 ,\n",
       "       11.098, 11.07 , 11.05 , 11.038])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5日均线\n",
    "end_price = df['close'].values # 收盘价\n",
    "N = 5\n",
    "weights = np.ones(N) / N # 生成等权重\n",
    "\n",
    "sma = np.convolve(weights, end_price)[N-1:-N+1] # 卷积计算\n",
    "sma\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "036b6436-eefd-45d9-aedb-5f1b58be42f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x228b9c233b0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sma,lw=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b759d62a-99a4-4201-bb4b-851ef90b65bb",
   "metadata": {},
   "source": [
    "#### 2.5.2 指数移动均线\n",
    "历史数据的权重以指数速度衰减\n",
    "\n",
    "计算股价与权重衰减的指示函数的卷积"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0dfddf-8cc2-40f4-a3f5-36788035aa20",
   "metadata": {},
   "source": [
    "```mermaid\n",
    "graph LR;\n",
    "    A(权重初始化) --> B(权重衰减);\n",
    "    B --> C(卷积运算);\n",
    "     C --> D(均线可视化);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52e7db73-61f3-4839-abfa-bf34bb086265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.08950718, 11.08878594, 11.09749909, 11.07339736, 11.03397341,\n",
       "       10.9987379 , 11.00356678, 11.03190796, 11.10468484, 11.12133521,\n",
       "       11.07553491, 11.00713098, 10.96934874, 10.94646376, 10.93751989,\n",
       "       11.02043349, 11.03762017, 11.05742931, 11.0339551 , 11.04362124,\n",
       "       11.06913763, 11.09964234, 11.14399851, 11.20835524, 11.27958584,\n",
       "       11.33463963, 11.38314527, 11.35214847, 11.2867194 , 11.24821698,\n",
       "       11.226107  , 11.19541418, 11.17362662, 11.19341997, 11.21603894,\n",
       "       11.20185313, 11.22402708, 11.25133645, 11.2886834 , 11.30315807,\n",
       "       11.3325457 , 11.36918371, 11.3956106 , 11.42044137, 11.36418871,\n",
       "       11.29251812, 11.14075781, 11.1244467 , 11.11318612, 11.08108244,\n",
       "       11.05294328, 11.0412557 ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "N = 5\n",
    "weights = np.exp(np.linspace(-1, 0 , N)) # 线性衰减\n",
    "# 归一化\n",
    "weights /= weights.sum()\n",
    "\n",
    "#计算指数移动均线\n",
    "ema = np.convolve(weights, end_price)[N-1:-N+1]\n",
    "ema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9bbb6227-ca1d-4f57-b337-31d8e4be3572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x228b9c220f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(N-1, len(end_price))\n",
    "\n",
    "plt.plot(t,end_price[N-1:], lw=1, label='end_price')\n",
    "plt.plot(t, sma,lw=1, label='sma')\n",
    "plt.plot(t, ema,lw=1, label='ema')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183f3743-97ea-4211-9b4b-84946b7d0879",
   "metadata": {},
   "source": [
    "### 2.6 股票时间序列\n",
    "* **金融领域最重要的数据类型之一**\n",
    "* 股价、汇率最为常见的序列数据\n",
    "* 趋势分析：\n",
    "  * 主要分析时间序列在某一方向上持续运动\n",
    "* 序列相关性\n",
    "  * 金融时间序列的一个最重要特征是序列相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda8e8aa-8416-4a17-85e1-166b348231ca",
   "metadata": {},
   "source": [
    "**Pandas时间序列函数：**\n",
    "* datatime：时间序列最常用的数据类型，方便进行各种时间类型运算\n",
    "\n",
    "**loc函数：**\n",
    "* Pandas中对DateFrame进行筛选的函数，相当于SQL中的where\n",
    "\n",
    "**groupby函数：**\n",
    "* Pandas中对数据进行分组，相当于SQL中的GroupBy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a519540e-25a0-4a8d-aa5e-c4161daf260a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-01-02</td>\n",
       "      <td>11.37</td>\n",
       "      <td>11.08</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.04</td>\n",
       "      <td>187660786.0</td>\n",
       "      <td>2.102923e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-01-03</td>\n",
       "      <td>11.09</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.01</td>\n",
       "      <td>119085841.0</td>\n",
       "      <td>1.320521e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-01-06</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.09</td>\n",
       "      <td>11.13</td>\n",
       "      <td>10.88</td>\n",
       "      <td>111954788.0</td>\n",
       "      <td>1.234306e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-01-07</td>\n",
       "      <td>11.07</td>\n",
       "      <td>11.16</td>\n",
       "      <td>11.18</td>\n",
       "      <td>11.02</td>\n",
       "      <td>77129461.0</td>\n",
       "      <td>8.583290e+08</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01-08</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.05</td>\n",
       "      <td>109567225.0</td>\n",
       "      <td>1.223599e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   open  close   high    low       volume         money  year  \\\n",
       "0 2025-01-02  11.37  11.08  11.41  11.04  187660786.0  2.102923e+09  2025   \n",
       "1 2025-01-03  11.09  11.03  11.19  11.01  119085841.0  1.320521e+09  2025   \n",
       "2 2025-01-06  11.03  11.09  11.13  10.88  111954788.0  1.234306e+09  2025   \n",
       "3 2025-01-07  11.07  11.16  11.18  11.02   77129461.0  8.583290e+08  2025   \n",
       "4 2025-01-08  11.15  11.15  11.28  11.05  109567225.0  1.223599e+09  2025   \n",
       "\n",
       "   month  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "file_name = './000001(20250101-20250328).csv'\n",
    "df = pd.read_csv(file_name).rename(columns={'Unnamed: 0':'date'})\n",
    "\n",
    "# 时间格式转化,并生成年，月列\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['year'] = df['date'].dt.year\n",
    "df['month'] = df['date'].dt.month\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "507416c7-952d-4d3c-b3fd-eb71f11df328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "close min : 10.75\n",
      "close min index: 14\n",
      "close min frame: date      2025-01-22 00:00:00\n",
      "open                    10.98\n",
      "close                   10.75\n",
      "high                    10.99\n",
      "low                     10.74\n",
      "volume            138933692.0\n",
      "money           1504818607.18\n",
      "year                     2025\n",
      "month                       1\n",
      "Name: 14, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 计算最低收盘价\n",
    "print(f'close min : {df['close'].min()}')\n",
    "print(f'close min index: {df['close'].idxmin()}') # 最低收盘价所在的行\n",
    "print(f'close min frame: {df.loc[df['close'].idxmin()]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8f66bbe2-c4da-4a65-aade-3d610914eb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month close mean : month\n",
      "1    11.042778\n",
      "2    11.190556\n",
      "3    11.208500\n",
      "Name: close, dtype: float64\n",
      "month open mean : month\n",
      "1    11.064444\n",
      "2    11.183889\n",
      "3    11.191500\n",
      "Name: open, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 计算每月平均收盘价与开盘价\n",
    "print(f'month close mean : {df.groupby('month')['close'].mean()}')\n",
    "print(f'month open mean : {df.groupby('month')['open'].mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b9b866bd-9945-4be7-b003-5f637e6c6ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>rise</th>\n",
       "      <th>rise_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-01-02</td>\n",
       "      <td>11.37</td>\n",
       "      <td>11.08</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.04</td>\n",
       "      <td>187660786.0</td>\n",
       "      <td>2.102923e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-01-03</td>\n",
       "      <td>11.09</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.01</td>\n",
       "      <td>119085841.0</td>\n",
       "      <td>1.320521e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.004509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-01-06</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.09</td>\n",
       "      <td>11.13</td>\n",
       "      <td>10.88</td>\n",
       "      <td>111954788.0</td>\n",
       "      <td>1.234306e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.005376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-01-07</td>\n",
       "      <td>11.07</td>\n",
       "      <td>11.16</td>\n",
       "      <td>11.18</td>\n",
       "      <td>11.02</td>\n",
       "      <td>77129461.0</td>\n",
       "      <td>8.583290e+08</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.006278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01-08</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.05</td>\n",
       "      <td>109567225.0</td>\n",
       "      <td>1.223599e+09</td>\n",
       "      <td>2025</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.000905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   open  close   high    low       volume         money  year  \\\n",
       "0 2025-01-02  11.37  11.08  11.41  11.04  187660786.0  2.102923e+09  2025   \n",
       "1 2025-01-03  11.09  11.03  11.19  11.01  119085841.0  1.320521e+09  2025   \n",
       "2 2025-01-06  11.03  11.09  11.13  10.88  111954788.0  1.234306e+09  2025   \n",
       "3 2025-01-07  11.07  11.16  11.18  11.02   77129461.0  8.583290e+08  2025   \n",
       "4 2025-01-08  11.15  11.15  11.28  11.05  109567225.0  1.223599e+09  2025   \n",
       "\n",
       "   month  rise  rise_ratio  \n",
       "0      1   NaN         NaN  \n",
       "1      1 -0.05   -0.004509  \n",
       "2      1  0.06    0.005376  \n",
       "3      1  0.07    0.006278  \n",
       "4      1 -0.01   -0.000905  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算涨跌幅\n",
    "# 今日收盘价减去昨日收盘价\n",
    "df['rise'] = df['close'].diff()\n",
    "# 涨跌幅比率：涨跌幅除以上一天的收盘价\n",
    "df['rise_ratio'] = df['rise'] / df.shift(-1)['close']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a832708f-cb02-4596-908d-74478d01e4f1",
   "metadata": {},
   "source": [
    "### 2.7 K线图\n",
    "* 能显示股价的强弱、多空双方的力量对比，是技术分析最常见的工具\n",
    "* 类库：\n",
    "  * matplotlib: Python的2D绘图库\n",
    "  * mpl_finance: 可以用来画蜡烛图、线图的分析工作，脱胎于matplotlib库，目前已经从matplotlib中独立出来\n",
    "  * 安装mpl_finance库 `pip install mpl_finance -i https://pypi.tuna.tsinghua.edu.cn/simple/‌‌`\n",
    "  * 带成交量的蜡烛图需安装mplfinance库`pip install mplfinance -i https://pypi.tuna.tsinghua.edu.cn/simple/‌‌`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "d3eb2863-acfe-432f-95f7-688a9972ee7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'K-line')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9YAAAHtCAYAAAD8y+b1AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAeXJJREFUeJzt3QmcXFWZ9/Gnqpd0yEaThZCQREVIGAN0wKhEAzQaYBLcX3dHUGdcGHRcRhHFQVQGcUR5RQQHHY06bsi4EiQsgQQTJWiaEDAM4ksSEkIW2ySddKe3ej/PSW6nqrqW5+mq7tp+33z6c7urb1fu/d9zbt1z77n3xBKJREIAAAAAAMCQxIf2ZwAAAAAAQNGwBgAAAACgADSsAQAAAAAoAA1rAAAAAAAKQMMaAAAAAIAC0LAGAAAAAKAANKwBAAAAACgADWsAAAAAAApAwxoAgDLT19cn/f39Az93d3dn/F0ikZDe3t6SLCMAADiChjUAAGVm69at8u1vf1tisZh84AMfkHXr1g38bvv27fKf//mfMnHiRPn85z8vf/rTnwb9vTa2P/zhD8sxxxwz8Nott9wi559//oitAwAAtSSW0NPdAACg7GjDesWKFXLOOecMvHbw4EG58cYb5e1vf7tMmTIl699qA/y4444LV7XVpk2bwtdZZ501IssOAEAtqS/1AgAAAJs9e/bIj370I7n00kulsbEx57xTp05N+XnWrFnhCwAAFB8NawAAKqR7+PLly+U973lPuJINAADKB/dYAwBQ5vQe61WrVsk73/nOITeqH3nkEbnooosGfl6/fr189KMflZ///Ofytre9TaZNmyb/8i//kvI3Dz74oHziE5+Qm2++WS677DJ5+OGHC14XAACqEfdYAwBQprQRfdNNN4UG8Ote9zr5r//6L2loaHD9vX7M7969W974xjeGJ4rfd9998uSTT8p5550nTU1N8uMf/1jmzp0r27Ztk5NPPjk0np/znOeEv3nta18r99xzT/g/9+/fLwsXLpQ//vGPw7rOAABUIq5YAwBQxubMmSO/+MUv5Ne//rW88pWvlI6ODvd76BPEP/nJTw78fMIJJ4Qr0fPnzw+NaqVXrE855RTZsmVL+Hnp0qXhPu3bbrst3Nf9s5/9TE466aTQwAYAAKm4xxoAgDL3ile8InQF//u//3tpbW2V22+/feCJ4Lt27ZI3v/nNA/M+97nPDUNrpaurq0v5OdOV7/r6+nBVW+lVbX0aefJ765PIAQDAYFyxBgCgAuiV5TVr1khXV5e89KUvDQ1fNWnSJLn77rsHvjI1qodCr2D//ve/L8p7AQBQ7WhYAwBQIY4//vhw5Xr69OmyYMGCYb3fWa9O//SnP5Vvf/vb0t/fH75++ctfyubNm4ft/wQAoFLRsAYAoMxo41WfxK1+8IMfyO9+97uB33V2dspb3vIW2blzp5x99tny7//+7/L444+n/P2mTZvki1/8Yvj+61//uvz2t78N7/PnP/9Z7rjjDmlrawv3ba9cuTJc4dZG8w9/+EP505/+JN/73vfC3+uY1zrPddddJ5MnT5ZFixaFh53NnDlzhNMAAKD88VRwAADKTHd3t8Tj8XBftDZ6e3p6QqNW6ff60a33Q+tTv3t7e8M8o0aNyvp+Bw8eDPdU6/z6fTSv/qx/q/8XAAAYOhrWAAAAAAAUgFPUAAAAAAAUgIY1AAAAAAAFoGENAAAAAEABaFgDAAAAAFCAeqkA+sTSbdu2ybhx48ITTAEAAAAAGE76nO99+/bJtGnT8o6gURENa21Uz5gxo9SLAQAAAACoMVu2bJHjjz++8hvWeqU6WqHx48dLuerr65Mnn3xSTjjhhDD2KHIjLzuy8iEvO7LyIS87svIhLzuy8iEvO7LyqYW89u7dGy7wRu3Rim9YR92/tVFdzg3rnp4e2bp1q5x22mnS0NBQ6sUpe+RlR1Y+5GVHVj7kZUdWPuRlR1Y+5GVHVj61lFfMcDsyDy8DAAAAAKAANKwBAAAAACgADWsAAAAAAApAwxoAAAAAgALQsAYAAAAAoAA0rItIBw2fOXNm3sHDcQh52ZGVD3nZkZUPedmRlQ952ZGVD3nZkZUPeaWKJRKJhFTA+GETJkyQPXv2lPVwWwAAAACA6uBph3J6ociDpK9bty5MkR952ZGVD3nZkZUPedmRlQ952ZGVD3nZkZUPeaWiYV1ko0ePLvUiVBTysiMrH/KyIysf8rIjKx/ysiMrH/KyIysf8jqCruAAAAAAAKShK3iJ9Pb2yurVq8MU+ZGXHVn5kJcdWfmQlx1Z+ZCXHVn5kJcdWfmQVyoa1kWkF/937twZpsiPvOzIyoe87MjKh7zsyMqHvOzIyoe87MjKh7xS0bAGAAAAAKAANKwBAAAAACgADWsAAAAAAApAwxoAAACoYguuuKLUiwBUPRrWAAAAAAAUgIZ1EdXV1UlLS0uYIj/ysiMrH/KyIysf8rIjKx/ysiMrH81p3Nix5GVA2fIhr1SxRAU8H90zMDcAAACAJK2tIitWlHopgIrjaYdyxbqIdHD0e++9l0HSjcjLjqx8yMuOrHzIy46sfMjLjqx8NKf29nbyMqBs+ZBXKhrWRRSPx2Xu3LlhivzIy46sfMjLjqx8yMuOrHzIy46sfDSnMWPGkJcBZcuHvFLRFRwAAACoZnQFB4aEruAl0tPTI7fffnuYIj/ysiMrH/KyIysf8rIjKx/ysiMrH81p9+7d5GVA2fIhr1Q0rIuMewx8yMuOrHzIy46sfMjLjqx8yMuOrHz6y7+DatmgbPmQ1xE0rAEAAAAAKAANawAAAAAAKq1hfc8998iTTz5Ziv8aAAAAAIDSNqzvvvtuufDCC1Ne6+rqkle/+tVy33335fzbZ599Vi6++OLwSPYTTjjBv7QAAAAAMmtuHvw1c+ah3+k00+8BFEW9Z+Zbb71VnnrqKeno6Bh4bdu2bbJ06VI5ePBgzr/VR5S/6lWvkltuuUVOPfXUoS8xAAAAgMxaWlJ/bmzUA3YRPf7u7k79XVvbiC4aUM1c41jv27cvPK5frzpHV6f1tXHjxoXX9Oucc87J+Lfvfe975bTTTpNLLrkk7/+jjfTkhrqOHzZjxgzZtWtXxvHD9Ap4XV2d9PX1hZ/1e31CXbZV09/r3+g8OtWvXI+Jr68/dP5B50/+Pp3+f3rSobm5OXzf398f5tdptGzpYrFYmCd52fV7/ZtMonVNXnbLuur65Vr2TOva0NBgWnadJ1pXy7JH76c//+1vf5MxY8aE9xqp7RSJ1q8StlNUtsaOHTuwLCO1nUpVnwrZTvp+um86+uijB/622uvTULdTetmqhfpUyHZSnZ2dYb+l/38t1Kehbif9+/b29kFlq5rrk2XZs61rcl1sbGysifo01O2k761Z6diyOn8t1CfzdjruOKk/+WQtUNKrDWotW7GYxNatk8S8eXrgf2hdtYEdi0nvn/4kDc88U3X1aajbKdrHH3XUUWG9a6E+FbKduru7Mx5DVE196umRAwcOhONJyzjWrivW2oDWhnX6a/n89a9/ldtuu02+9KUvyS9+8QvZunWrvO997wsLnMk111wjV1111aDXly9fHgp6upkzZ8q8efNk/fr1Mnr0aJkzZ448+OCDsnPnzozv39LSIrNmzZKVK1fK3LlzZcqUKeG9s23s1tbW8L7Lli2TxYsXhwq3YsWKjPPqRtZ59CTAhg0b5Nxzz5UtW7ZIW5YzgpMnT5YFCxbIE088Ed43Wo/NmzdnnH/27NkD6zd9+vSB9dBGRCZnnnnmwPotXLhwYD2yidZv1apVsmTJkrAea9asyTivbvto/XSbRuvx+OOPZ5w/fTvpujz22GPh/yjFdorWj+1U3vVpqNtJd4Snn34626nMt1Ml1id9T10/7bE1kttpwRVXyOrPf75itpOu+7p161J6uSWjPpXHdip1fRrKdnrkkUfCyQdtWLOd0rbTV74iC2+9VUZ3dMiypItZYf/x/vcP/Lz461+XzrFjZdVXviJLRKhPSdtp/vz5Yf30VtdaqE+FbKe77rqruuvT8uXhwvCwXLFW2hU8+Yp1JNcVax04/Prrrw/hqy9+8YvhrMLll19eVVes9T10HbXw6P9Rbmdcyu3MmM6nlW3RokXh/yrnM5il3k5R2YqyKvczmKXeTvpheOedd4a6qMtTC/VpqNspvWzVQn0qZDvpPPpBe8EFF4T/ZyTrU92iRdJ3+HO0EraTzmvZx1dTfbIse7Z1Ta6LehGhFurTULdT8j5e36OSPp9KccW6p6FB9m/aJGNmzZKGw+vPFevc+/jzzz8//F0t1KdCtpNexMh0DFGrV6xHpGH9ox/9SH71q1/Jf//3f4ef9Uzju971Llm7dq3p/9SGtZ6VtKxQKWmUUeHJ1O0NqcjLjqx8yMuOrCoor9ZWkSxn+8sRZcuHvOzIKgd9GFnaPdZ6oJ94+GGJnXaaDEpLrwi2t4/kEpY1ypZPLeS119EOHZHhto4//viU7r7RGYNqpN0RYEdedmTlQ152ZOVDXnZk5UNedmTlEItJf11dmCI/ypYPeY1Aw1q7ct94441h+pKXvCT0k9eHmERXrLWLRbXRkwV670C1njQoNvKyIysf8rIjKx/ysiMrH/KyIyuf3oYGaZ86NUyRG2XLh7wKaFjrzew6tJbeKK73H0Tuv/9+eeihh8IDyrZv3x5e0+nVV18dpto9QLuBf/azn5Uf//jH8uijj8qnP/1pz38NAAAAAEBZcj0VXJ/AduWVV4avZGeffXZ4glsyfdqaPjE1ok9r0y8AAAAAAKrJiNxjDQAAAABAtaJhDQAAAABAAWhYAwAAAABQABrWRRYNgA4b8rIjKx/ysiMrH/KyIysf8rIjK594f3+pF6FiULZ8yOuIWEJH9q6igbkBAKhKra0iK1aUeikAlLPmZpGWlsGvt7Vlf/3wcLgACmuHcsW6iPr7+2XHjh1hivzIy46sfMjLjqx8yMuOrHzIy46sfPpjMeluagpT5EbZ8iGvVDSsi0gLlQ47RuGyIS87svIhLzuy8iEvO7LyIS87svLpr6+X/RMmhClyo2z5kFcquoIDAFAJ6AoOIB+6ggNFRVfwEtGzNZs2beKsjRF52ZGVD3nZkZUPedmRlQ952ZGVT388Ll1jxoQpcqNs+ZBXKmpYEfX19UlbW1uYIj/ysiMrH/KyIysf8rIjKx/ysiMrn776etnX3BymyI2y5UNeqWhYAwAAAABQABrWAAAAAAAUgIY1AAAAAAAFoGENAAAAVMLIAADKFg1rAAAAAAAKQMO6iGKxmEyePDlMkR952ZGVD3nZkZUPedmRlQ952ZGVT6y/Xxq7usIUuVG2fMgrVSyRSCSkigbmBgCgaruBrlhR6qUAUM77gOZmkZaWwa+3tWV/vb29eMsIVBlPO5Qr1kWkY7ht3LiRsdyMyMuOrHzIy46sfMjLjqx8yMuOrHz66upk//jxYYrcKFs+5JWKhnWRdXZ2lnoRKgp52ZGVD3nZkZUPedmRlQ952ZGVTz+NajPKlg95HUFXcAAAKgFdwYHaRldwYMTRFbxEtBvEunXr6A5hRF52ZOVDXnZk5UNedmTlQ152ZOWjXcD3NTfTFdyAsuVDXqloWBdRf3+/bN68OUyRH3nZkZUPedmRlQ952ZGVD3nZkZW/G3jXmDF0BzegbPmQVyoa1gAAAAAAFICGNQAAAAAABaBhDQAAAABAAWhYAwAAAABQABrWAAAAAAAUgIZ1EcXjcZk9e3aYIj/ysiMrH/KyIysf8rIjKx/ysiMrn3hfnxy1d2+YIjfKlg95pYolEomEVNHA3AAAVKXWVpEVK0q9FABKVY8tf9vcLNLSMvj1trbsr7e3D215gBqw19EO5fRCEfX29srq1avDFPmRlx1Z+ZCXHVn5kJcdWfmQlx1Z+fTW18ueSZPCFLlRtnzIKxUN6yLSbhDTp0+nO4QRedmRlQ952ZGVD3nZkZUPedmRlU+8v19GdXaGKXKjbPmQVypOXRWRFqpZs2aVejEqBnnZkZUPedmRlQ952ZGVD3nZkZWPNqib9u8XoWGdF2XLh7xScXqhiLQbxL333kt3CCPysiMrH/KyIysf8rIjKx/ysiMrn96GBmk/9tgwRW6ULR/ySkXDuoj0OXD79u0LU+RHXnZk5UNedmTlQ152ZOVDXnZk5ZOIxUKjWqfIjbLlQ16paFgDAAAAAFAAGtYAAAAAABSAh5cBAAAAxaTjSWfS0ZH5d4wlDVQ8GtYAAABAsbW0DH6trW3w6/oagIpHV3AAAAAAAApAw7qI6urq5MwzzwxT5EdedmTlQ152ZOVDXnZk5UNedmTlU9fTIxN27QpT5EbZ8iGvVHQFL/Ig6VOmTCn1YlQM8rIjKx/ysiMrH/KyIysf8rKr+qyKfH92PJGQxq4uHRupSAtYvaq+bBUZeaXiinUR9fT0yO233x6myI+87MjKh7zsyMqHvOzIyoe87GoiK70PO/1r7NjBrxn0NDbK7mnTwhS51UTZKiLySkXDuojq6+tl4cKFYYr8yMuOrHzIy46sfMjLjqx8yMuOrHzqtSv4jh1hitwoWz7klYqGdZGNHj261ItQUcjLjqx8yMuOrHzIy46sfMjLjqwcEgmJ9/XRFdyIsuVDXkfQsC6i3t5eWbZsWZgiP/KyIysf8rIjKx/ysiMrH/KyIyufXu0KPn16mCI3ypYPeaWiYQ0AAAAAQAFoWAMAAAAAUAAa1gAAAAAAFIBHuAEAAADVoq0t9eemJpFJk0TWrxfR8awBDAsa1gAAAEA1aG8f/FpPj6xetkwWL14s0tBQiqUCagJdwQEAAAAAKEAskSj/Qe327t0rEyZMkD179sj48eOlXGmU+rh5HSQ9FouVenHKHnnZkZUPedmRVQXl1doqsmKFVArKlg95VVlWzc0iLS2Zu2mnv66vJV9pLuRvKzWvMkFWPrWQ115HO5Su4EXW2dkp48aNK/ViVAzysiMrH/KyI6syy0sPqjPp6Mj+uzwH1q1LW2XFRSPfKKds+ZCXXdVnlX6fdLQPyPS6QdXnVURk5UNeR9AVvIj0jM2qVasYJN2IvOzIyoe87MiqTPPSq1LpX2PHZn69TFG2fMjLruqz0hNlmb5e9rLMr9d6XkVEVj7klYqu4AAAlBNPN9Do9TK9Yg3UrCJ3567E20GAauBph3LFuoj6+/tlx44dYYr8yMuOrHzIy46sfMjLjqx8yMuOrHzIy46sfMgrFQ3rIurr65M1a9aEKfIjLzuy8iEvO7Ia4bz0itMwa762edDXA5sfyPi6fg0XypYPedmRlQ952ZGVD3ml4uFlAABUmZapqV1N27a3DXoteh0AABSOK9YAAAAAABSAhjUAAAAAAAWgYQ0AAAAAQAFoWAMAAAAAUAAa1kUUi8Vk3LhxYYr8yMuOrHzIy46sfMjLjqx8yMuOrHzIy46sfMgrVSyRSCSkigbmBgCg5JqzDGPV0SEydmzm37W3H/nblsFP8Ja2tuyvR397eLgtz1PB2y878rcAisRTj9PqcM7h+lasKN4yAihqO5Qr1kWkg6Nv2rSJQdKNyMuOrHzIy46shjEvPXhO/9JGdabXqxBly4e87Go2qyE2qms2ryEgKx/ySkXDuoi0UG3dupXCZURedmTlQ152ZOVDXnZk5UNedmTlQ152ZOVDXqnq035GAerr62XBggWlXoyKQV52ZOVDXnZk5UNedmTlQ152ZOVDXnZk5UNeqbhiXUR9fX3y4pteHKbIT3PauHEjeRmQlQ952ZGVD3nZkZUPedmRlQ952ZGVD3kV2LC+++675cILL0x5raurS1796lfLfffdl/fvDx48KG94wxukGmk3iAMHDtAdwkhzevzxx8nLgKx8yMuOrHzIy46sfMjLjqx8yMuOrHzIq4Cu4Lfeeqs89dRT0qFPNT1s27ZtsnTp0tBgtrjppptk7dq1nv8WAAAAAIDqaFhfcMEFsnv3brn99tsHXtOxyy6//PJwtsLSMF+8eLFcf/31OefTRnpyQ10fc656enrCV7p4PC51dXUD3RD0+97eXsk2kpj+Xv9G59GpfmV63+T7B5TOn/x9uuT30DM3+qXz6zRbFwkd903nSV52/T7bmZ9oXZOX3bKuumy5lj3TujY0NJiWPXldLcue/n7Zsh+u7RSJ1q8StlO03tF0JLdTqepTMbaTqpX6NNTtlF62aqE+FbKdovfReaJsMi57U5NIY6PE+/qkrq9P+urqDi2bvkd9vSTi8cHz9/QcWnadp6FB4rq+iYT0NDYe+ttYTPoOfz+w7Ie3W2/SujbFm6RRUueLSSy81i3d4ft6qZce6ZE6qctaXwvdTtZ9fDXVJ8uyZ1vX5LpYK/WpGMcRZfv5dHgfMLCuOo8uq+4/6uoG9gv6fVT/h2s7ZdrPV3t9SuapT9H76O+T16MW6lMh2ynT9qqvkuMIz8jUroa1NqK1YZ3+mkVbW5scddRRctJJJ+Wd95prrpGrrrpq0OvLly8P75Fu5syZMm/ePFm/fr2MHj1a5syZIw8++KDs3Lkz4/u3tLTIrFmzZOXKlTJ37lyZMmVKeO9sG7u1tTW877Jly8KJgc7OTlmRZ8iDXbt2yYYNG+Tcc8+VLVu2hPXPZPLkyeGm/yeeeCK8b7Qemzdvzjj/7NmzB9Zv+vTpA+uxb9++jPOfeeaZA+u3cOHCgfXIJlq/VatWyZIlS8J6rFmzJuO8uu2j9dMnAkbrke0kS/p2OuGEE8Lrd91114hvJ62M0fpV0naKshrJ7VTK+jTU7fTkk08OvFYr9anQ7ZReD2uhPg1lO409PAa1bqPt27dn30433xwmMzdskHl33y3rNc+ODpnzhz/Ig698peycNWvwmy9bdmg7icjK17xG5v7whzJlwwZZftNN0jt6tCy44gpZfcklKX/S+slPyuimppTtdPNJh/7vZFfsukLePeHdctOem2Rm/Uw5a/RZ8r1935OXTnhp1myi7bTgPxfIzS+52b2dpk2blnMfX831KZ2nPunrtVKfhrqdHn300YHXyvbz6fA+IDJ50yZZ8LOfyYHx4+V/W1sH9gub5849NENapsOxnZLrYq3UJ+/n06RJk8JUjyO6u7troj4Vsp2i3DPt51ur5DjitNNOE6tYwtMMFwldwS+++OJB91Pra/p1zjnnDPobveJ8yy23yEc/+tHw83Oe85zwPp4r1jNmzAhhZxqYu1zOjOl76MPL/vjBP4b/o9zOuJTbmTGdTyvbokWLwv9VzmcwS72d9HXdaUVZVcoZzFJtJ33uw5133hl25Lo8tVCfCrlinVy2aqE+FXrFWj9otQeX/j9Zt9PMmSKnnjr4ivUf/iC9L3zh4CvW69eLbN5clPo07UvT5NQpp6bMt3b7Wpk/df6gK9aPbH9EnvnIMzm30znfOUfu+Yd73NtJ57Xs46upPhV6xTqqi3oRoRbq01C3U/I+Xt+jLD+fDu8DBl2xbmuT/jPOSL1ifbj+D+cV6/T9fLXXp0KuWOs+/vzzzw9/Vwv1qZDtpM+WynQMUU3HEbqORx99tOzZsydjO3TEh9v6/ve/Lw888MDAvdV6JuTNb36z/Mu//Es4I5Bu1KhR4StT4Jk+nJNDiEQbMJfkeXK9b6Z58s0fbbT07y3Lrt8n/5xv2S3r6ln25Hksy548j2XZo99HhT/fdmU7pf4+0zzDuZ2sy852yj9/OW+nbPWQ7ZSZ/j5ahozL3tUl0t098KMeRA8se6YDC50/w4kN77KHt+rvCg3oZAlJDLym32ujWvVJX9731wOYaP082yk64Mm3j6/G+pRv2XPNn2n9aqE+Vdp2imTdTmn7gEgsaX+g0/B9Wv0fru2UqS7W/HbKInn9qE/5t1Ou/XxDhR9H6Geg1Yg0rC+55JLwFdEr1j/60Y+k2ujGbxrVlLcQ4BDNSbuhkFd+ZOVDXnZk5UNedmTlQ152ZOVDXnZk5UNeqYYtBe3KfeONN5qfFl4N9CzJ2HFj854twSGak94TQV75kZUPedmRlQ952ZGVD3nZkZUPedmRlQ95FdCw1i7cOrSW3iiu9x9E7r//fnnooYfktttuCw9zUTq9+uqrB36uBdrtrWNfB4OkG2lO69atIy8DsvIhLzuy8iEvO7LyIS87svIhLzuy8iGvArqC6xPYrrzyyvCV7Oyzzw5PcEumT1vTMa4zyfXgskoXr6MrhIc+LRA2ZOVDXnZk5UNedmTlQ152ZOVDXnZk5UNeI3yPda3QbhD6JE+6Q9hoTvroe+RHVj7kZUdWPuRlR1Y+5GVHVj7kZUdWPuSVisurRaSPbddHsed6lDyO0JxWr15NXgZk5UNedmTlQ152ZOVDXnZk5UNedmTlQ16paFgXkY6FpuOdOYcGr1mak963T175kZUPedmRlQ952ZGVD3nZkZUPedmRlQ95paJhDQAAAABAAWhYAwAAAABQAB5eVoDma5tTfm6KN8mk+CSZef1M6ervGjR/+2XtI7h0AAAAAICRQMO6QC1TWwa+b5RG2bZrm5w65VTplu6U+dq2t5Vg6QAAAAAAw42u4AAAAAAAFICGdRH1Sq+097WHKWxj37W0tDDutwFZ+ZCXHVn5kJcdWfmQlx1Z+ZCXHVn5kFcquoIXUb/0y/7E/jBFfvF4XGbNmlXqxagIZOVDXnZk5UNedmTlQ152ZOVDXnZk5UNeqbhiXUQN0iDH1h0bpshPB5O/9957GVTegKx8yMuOrHzIy46sfMjLjqx8yMuOrHzIKxUN6yLSLuB7+vfQFdxxlmvu3LlhitzIyoe87MjKh7zsyMqHvOzIyoe87MjKh7xS0RW8iBKSkK5EV5giP62EU6ZMKfViVASy8iEvO7LyIS87svIhLzuy8iEvO7LyIa9UNKyLSIfbmlY3LUzTh9vCYD09PbJ8+XI577zzpKGB7vO5kJUPedmRVXXmlT7EY0d3h2nYx+Zrmwe9pn+b6XXVfll7zqzOuOEM+cMH/lDWWZWLSilb5YCsfMjLjqx8yCsVDesii8foCuHBPRl2ZOVDXnZkVV15ZWrsti5tlRUXrTD9fcvUlpSftUGe/lr0ej6JBD24qqlslROy8iEvO7LyIa8jaAUCAAAAAFAAGtYAAAAAABSAhjUAAABGnN6iAADVgoY1AAAAAAAFoGENAAAAAEABeCp4EfVIj2zv3S6TZXKpF6Ui1NfXS2tra5giN7LyIS87svIhr+zSh+SKSUwO9hyUY798rCQk4RqqayhPM690Wqau23WdvKr+VVKNijmUm2a15LrrpO5V1ZlVsbHfsiMrH/JKRQpFpAcOfdKX8QACmY0ePbrUi1AxyMqHvOzIqvrzGqnGafqwXA9vf1hOm3rakIbqqkXxeHV3JCzmUG7VnlWxVeJ+q1TIyoe8jmCvVESN0ijT66eHKWzj3i1btozx7wzIyoe87MjKh7zs+Ez00TK1+6+7KVvWrHaTlRX7LTuy8iGvVDSsi6hbumVr79YwRX7abWTx4sV0HzEgKx/ysiMrH/Ky4zPRR8vUxGMmUrasWU0kKyv2W3Zk5UNeqWhYF5HeT1YndWEKm87OzlIvQsUgKx/ysiMrH/Ky4TPRr7+/XypO6wgMmdXcnPo1caL0r10bpoN+p1/5FrkGh/kqZL9Va3mxj/chryNoWBdRgzTI1PqpYYr8tNvIihUr6D5iQFY+5GVHVj7kZcdnoo+Wqfa/tVO2smlpGfjqnT9f2p/3vDBNfj18YRD2W3Zk5UNeqWhYAwAAAABQABrWAAAAAAAUgDvNAQBA1qGOdKxhhsdCkOn+5Y6O7Pc1t+cfq7ySxs8GgFxoWAMAgKyNCn1w0UiNg40KkH4fc1tb5nub9fUqHD8bALKhKzgAAAAAAAWgYV1k/YkKHCqjhBj3zo6sfMjLjqx8yMuOz0SfWIyhyazilTg0WQmx37IjKx/yOoIkiqhbumVb3zaZIlNKvSgVoaGhQZYsWVLqxagIZOVDXnZk5UNednwm+svWxIkTwxS5NXR3y8Rt20SmULYs2G/ZkZUPeaXiinURxSQmTbGmMEV+/f39smPHjjBFbmTlQ152ZOVTi3kN9f5qPhN9tEx1d3fXVNkaqv5YTLqbmsIU+dXifmuoyMqHvFLRsC6ieqmXCfEJYYr8tBJu2LCBymhAVj7kZUdWPuRlx2eij5ap/fv3U7YM+uvrZf+ECWGK/Nhv2ZGVD3mlomFdRD3SI8/2PRumsN2Tce6553JvhgFZ+ZCXHVn5kJcdn4k+Wqaam5spWwb1PT3S/OyzYYr82G/ZkZUPeaUihSKKS1zGxMaEab9w5iYfPbu1ZcsWmTFjhsTjnOPJhax8yMuOrHzIK7fk4Yrq9F9fnTyy/RHpk76SLlellK2urq4wpWzl1h+PS/eYMdIYj/MQMwP2W3Zk5UNeqWhYF5F2d2uuaw5TfWgLcuvr65O2tjaZNm0alTEPsvIhLzuy8iEv+xjYPT09cvpXT5c/fvCPPJDLWLY6OjrClLKVW199vexrbpaj6+sl3s3xVj7st+zIyoe8UpEAAAAAAAAFoGENAAAAAEAB6AqOgrUubR3ycCwAADRf25zx9Y7ujoy/S+92XlZaW0VW8JmY7x78aPumv5b9j5Pma2oSmTRJZP16ka6uIi9l7fLWw7Kvi8AIo2ENAABKrmVqy6DXtNGV/rq5IYaykqkBZj4x3572t/o08NNPF9m8WYT790tSD6PXARxBV3AAAAAAAApAw7qIdIitrv4uhtoyisViMnny5DBFbmTlQ152ZOVDXnaakT4NnKxsyMtOM2okKzP2W3Zk5UNeqegKXkS90iu7+nfJ8XJ8qRelIuhg8gsWLCj1YlQEsvIhLzuy8iEvX1YTJkwI06rUnPmeU+noyPy79O7MtZZXEUVZCVmZsN+yIysf8krFFesiqpM6GR8fH6awjX23cePGMEVuZOVDXnZk5UNedprRgQMHqjurlpbBX2PHDn7NoCbyKhLNaD9ZmbHfsiMrH/JKRcO6yGhU+3R2dpZ6ESoGWfmQlx1Z+ZCXXX8ft0Z5kJddfz9ZebDfsiMrH/I6gj40RdQnfdLe3x6myK+urk7mzZtX6sWoCGTlQ152ZOVDXr6sxo4bG6bIj7zsNKNx2jOArEzYb9mRlQ95peKKdZGvVjfHm7lqbaTdRtatW0f3EQOy8iEvO7LyIS87zahjXwdZGZGXnWa0r2MEstJ75TN96RBfmV4vU7W439Kh3IaiFrMqBHmlomFdRNqgHhMfQ8Pa0Y1r8+bNdOcyICsf8rIjKx/ystOMug52kZUReTmz6hqhrIp4H32psN+yIysf8kpFwxoAAAAAgALQsAYAAAAAoAA8vAwAgOHQ1pZ5jONMr6O8tLaKrFhR6qVApWMfANQUGtYAABRbe3vm12mwAQNWXDT0urD685+XxVLG2AcANYeu4AAAAAAAFIAr1kWk41fv7d8rx8qxpV6UihCPx2X27Nlhitw0o1f8+79L/MILS70oFYGyZUdWPuRlpxkdddRR5qzatg/uHtvR3ZHx9WrkzauWeethehmqpXKl2G/ZkZUPeaWiYT0MDWudwjao/Jw5c0q9GBWT1ZijjtJvSr0oFYGyZUdWPuTly0obijrNp/2y9qxj0RbSXbha86p1nnqYqWzVUrlS7LfsyMqHvFJxeqGI6qVeJsUnhSny6+3tldWrV4cpctOM9uzZQ1ZGlC07svIhLzv2Wz7kZUc99CEvO7LyIa9UNKyLqF/6pTPRGabIT7uNTJ8+ne4jBprRqFGjyMqIsmVHVj7kZcd+y4e87KiHPuRlR1Y+5JWKS6tFpA3q/Yn9NKyNtBLOmjVLasoQnwaqWTU1Nek3w7JY1aYmy9YQkZUPefn3W6U64Crn7r7N1zZnfF3v/Z34HxNd3eVrEfXQh7zsyMqHvFJxlF5EDdIgx9YdG6bIT7uN3HvvvXQfMdCM2tvbycqIsmVHVj7kZcd+K7eWqS0pX/OnzpcTRp8Qpum/QyrqoQ952ZGVD3mlomFdRDGJSUOsIUyRXyKRkH379oUpctOMevv6yMqIsmVHVj7kZacZ9bHfMuMYwo566ENedmTlQ16paFgDAAAAAFAA7rEGylFz2v13en/1pEkiM2eKdHUNnr+de+8AAACAUqFhDZSrlqT76hobRbZtEzn1VJHu7tT52tpGfNEAAAAAHEFXcAAAAAAACkDDGgAAAACAAtAVvIh6pEd29e2SyTK51ItSEerq6uTMM88MU+RW19MjE3btkrrJlC0LypYdWfmQl51mNH78+GHPKteY0Jl+V67jQRd6DFHO43YXG/XQh7zsyMqHvAq8Yn333XfLhRdemPJaV1eXvPrVr5b77rsv69+tXbtWXvOa18iLXvQiectb3iIdHR1SbRKSkK5EV5jCNqj8lClTwhS5xRMJaezqClPkR9myIysf8rLTjBobGwvKytpQTB/3Wb/GNo6tqPGgOYaokXq4YuRPflR0XiOMrHzIK5UrhVtvvVXWrVuX0ijetm2bfOUrX5GDBw9m/bvu7m751a9+Jbfddps8+OCDcvTRR8vnPvc5qTaN0ijT6qaFKfLr6emR22+/PUyRW09jo+yeNi1MkR9ly46sfMjLTjPavXs3WRlxDGFHPfQhLzuy8iGvArqCX3DBBeFDUgOMjBs3Ti6//HJ5/PHHs/7dM888I5deeulAN4FXvvKVcvPNN2edXxvpyQ31vXv3hqlutEwbTs+S6Hv39fWFn/X73t7erIOV6+/1b3QenepXrgJRX38oJp0/+fumeFPKB2BMYqEb17FybPi+XupD1664xMO8mf6PWCwW3jN52fX7/v7+jMsSrWvyslvWVf/v5GW3rGtDQ0NYjmjZsi27nlyPsrEse/R+Ov+CBQvCsmfKpljbKZto/fRL57esa6HbSa849+VY15TtlNSITsRiMn7XLknMmBEa1/X6ZPBYTHobGqRBxLTsyevq2U4jVZ+KuZ10qmXLuq7lVJ9Gejvp/NqNK70eVkJ9KsV2UgsXLhxYBvd2Ovz/VFJ9Gup20r+dMH5C3n18odsp/bNYRVd944f/9Uqv1Eld1s/iIW0nnaehQeKH9+3Ric+6WEz60k6C1sdiIonEwHbSY4T0ZY6OIWbIjIGfo+MIXfZs5SDaTrqsuvzDWp90PdI+n9LXd+Dzqalp2D6f9O9e+tKXhvmHtN/rz1wmB9UnXY8MJ7SjdW7o7pb+WEz663VLSdnu9zLt5zPVp0x1KSy7xAqrTyU4jkjexp79XrSP1+017PWpCo4jsh1DlPvnk2c7ZZun4Ia1NqK1YZ3+Wj6zZs1K+fmRRx6Rl7/85Vnnv+aaa+Sqq64a9Pry5cvlqKOOGvT6zJkzZd68ebJ+/XoZPXq0zJkzJ1wZ37lzZ8b3b2lpCcu0cuVKmTt3bujCoO+dbWO3traG9122bJksXrxYOjs7ZcWKFXLzSYNPDnxq56fCB/qs+lly1uiz5Hv7vicnN54sHzzpg+Hv002ePDk0AJ544onwvtF6bN68OeOyzJ49e2D9pk+fPrAe+/btyzi/FvZo/XRHEa1HNtH6rVq1SpYsWSK7du2SNWvWZJxXt/25554rXQe7wvJE65HtJEv6dtJ1eeyxx8L/MZzbKROtjNH6bdiwIazHli1bpC3L0FXF2k6n7N4tqzPkP2g7NTXJsksuSZlnwRVXyOr3vz98v/h975POY46RVZ/+tCx58EHTdnrpLS+V6065zr2dRqo+FXs7HThwQE4//fQRr09hO33+80OuT7p+W7durZntVKr93pC309atcs6HPyybL7hAts2fLwuuu06eeM1r5PHXvMa2nURqZjvpuu/r2Cd33HHHsH4+Zfos3t23W9ZuXxs+f09qOEl+tv9nMr9pvnzgpA9k/D+GtJ1EZOWb3yxzV66UKZs2yfJ3v1t6R406VLbS9t+tn/ykjO7tHdhOxzUeJ5ccnTqPumLXFfKuCe+Sm/bcJDPrZw4cR7x0wkuzZhNtJ81Gl38o9WnfC18oKzMcdw3aTscdJ6MbG1M+n9LXd/HXvy6dY8fKqq98RZaIDMt+T48j9TaDCRMmDKk+7f7r7ox5DqpPxx0nK9K2ZbTOD7773bLkpptk18yZsuGss+TctWvLfr+Xrz5lqkvqsp2XhemQ61MJPp+ibTyU/d78+fPDdtJbXctxO5XTccRdd91VkZ9Psxzb6bTTThOrWMLTDBeRp556Si6++OJB91Pra/p1zjnn5Px7bZh/8IMflO985zvhzIT1ivWMGTNC2PoglHI5Mzbz+ply6pRTB+bTa4ebdm2SaZOmhTPMyVesH9vxmGz+0OaqunITLXvrd1rlrrff5T4zpvNpZVu0aFHGslBNV24G1uMVr5C+u+5ybyd9ff+LXyxjfv/7sNwjuZ0q8Yq1fhjeeeedYUeuyzOS9alu0aKBbVzuZ5qjXkD6wZheDyuhPg37dpo589DY8Ul66+ulY/NmGX/88VKn2fT2Sl9dnfRrj6z160XSPtSroT4NdTvpvJZ9fKGfT+mfxUpPcGvD+vSpp6dcYXt0x6MZP4sHbadXvEJ677or93aaOFF6588ffMV67Vrpmz8/ddnXrtUDoIHtNPGLE2X+1NR5omOIWZNmyX7Zn3LF+pHtj8gzH3km53Y65zvnyD3/cM+Q6lP/Oedk/GwatJ2OPVbktNNSr1inre/AFes//UkannlmWPZ7yft4fY9hq0+Ht/Gg9z28zilXrNeulf7du8tyv5dpP5+pPk2/bvqguqS0Lp029bRBV6zN9akE+71F318Ujnm8+z19H21MnX/++eHvKvG4fCSPI/QiRqZjiHL/fPJsJ11HvY15z549GduhJXsquC7c//2//1duuummrI1qNWrUqPCVTv8m198lP5Eu2oC5JM+T630zzaPfd/V3Sbd0Z5xXP9T1w1D1S3+Y17rs+n2+p+slL7tlXdOX3Tp/VPhyih1ZBsuyR7+PCn++7Vrodsolef0s61rwdorFJJ5jmfIte3pWI7Gd0tejJrZTIcueYRt7tlPyPCO5nbLVw5reTl1d+pCQjO8d1w/+wwcIddpdUD/cdf4sGdVifYoOePLt4wv9fMr1Wdx/+F9YHulzfRabtlPSwb82soJEQuLp5UYP3mKxgf9bjxGyLXN07JB8HKHLni8bPdCMlt9bn+J5PpvC+unvDx+EDqxrjvVt0PpQ5vu9bAbm0fXItA9IWmc9qRI/XA7Kfb+XqS4m16dsdSm6tWLY61MR93ux+JH6NtT93lDrU6mPy0tRn3Lt5xvK8PMp/ftsdHl132o1Yg1rbUTdcMMN8rGPfczUfRwAAAAAgEowbM9G167cN95440CX7i984Qvyjne8Y6BR/eMf/zg8LRyDx6EEAABAGdB7PNO/dHSc9NeQEce1qCWuK9Z6M/vSpUvDjeJ6/8F5550XXr///vvloYceCo1mvTF86tSpsn37drn66qvDmNd6P7Y2sn/yk5+E+bU/u77Xm970puFZKwAAAKAQ7e2ZX29tLcl41ACqqGGtT2C78sorw1eys88+OzzBLZk+bU3HuFYXXXRR+AIAAAAAoNoMW1dwAAAAAABqwYg+Fbza6ZMUt/ZulUkyqdSL4leCbk36ND4dKsPyVL5aF4ZnmThRYoasmq9tzvh6R3dHxt+1X5alq1sFo2zZkZWPDiU0cetWiU2qwP38CKuJspXp3tro/tt8f7p98DwHug/I09ufLtbSVa2aKFtFVLF5cWxa9sgrFSkUkY45qeP66TQamgC56aDuPCXe/mT93IMIHNEytSXjQVz665kO7KoFZcuOrBx0zFod0kOH38gy/iVqpGwVcP9tphOa+vyZs//rbLn/Xfe7hnepVVVdtoYBedmRlQ95HUFX8CJqkAaZUjclTJGfDsy+atWqnIPF4xDNaM/f/kZWRpQtO7Ly6W1okD1TpoQpcqNs+WhOf9vDft6CsuVDXnZk5UNeqbhiXeSu4Nv6tskUmVLqRakIOuj6kiVLSr0YFZOVdgUXDuZNKFt2ZOXToF3B9cGcU9jP50PZGtp+XqfIjbLlQ152ZOVDXqloWBeRdgFvijXRFdzRtXnXrl0yadIkicfpPJEvq97ubqnv7y9JVjoO5YqLKmdokUotW6XIuVKzKpX+WEx6m5qkPhaTOF3BK79sNWd+JkW4TzrT77J1/y5SXt3d3WGaKy/vczSK/iyN9PvHjfeU11zZKiMjkVfB5TJTfctWD4exLmpWC7+1UFa9exVly4C6mIqGdRFpF/BJdZPCVK9eI7e+vj5Zs2ZNeOgBlTF/Vnv27pWj+/rIyoCyZUdWPn3aFXzSJDm6oUHi3eznq6JstQx+JkVoKKa/PsyNR81r7969YZovL+tzNKLXiyZTY6YED5gqedmqsDGsRyqvgstlpjqXrX6WQT1EGdTFMkMCAAAAAAAUgIY1AAAAAAAFoCt4rSmj+8kAAIBfpm60ei9r3m7f3mMAxXEAylyme7hH7JkDQBIa1rWoTO4nAwAAPtkaBeaHH1qPAaLXgQqQfh/3iDxzAEhDV3AAAAAAAArAFesi0iG2ehI9VTvUlp4Nz0TP/mX7XXT2PFN3nFGxUTI+Nl7e+/h75WDi4KDf01XniFgsJvV1dWE6nLzDZZTrNtKcxo0bN+x5VUPXtaJkVYKnApfq9pdYIiH1PT1hisqvh+VEc6obgf18NaBsDV9eQ77NoEpQD32oi6loWBdRj/TIs33PynFynFSjbF3MrN3PSjY8SBWor6+XZm0E1A9/lbVup3LeRprXueeeK+WuHLquVUpW5XL7izaqm599VuS46tzPF1NNlK0i59X2ofLdr5YTytbw5FXwbQZVdLylU+RHXUxFV/AiiktcxsTGhCnyIy+7/v5+6erqClPkpzlt2rSJvAzIyqc/HpeuMWPCFLlRtnzIy46sfMjLjuMtH8pWKo4MikgbiKNjo2koGpGXne6wDh48yI7LSHPaunUreRmQlY82qA+OHk3D2oCy5UNedmTlQ152HG/5ULZS0c+hiHqlV3b175Lj5fhSL0pFIC9fV5sJEyaMSFfwaslrwYIFpV6MikBWeaR1D9caOEHvz961q2SLVCkoWz7kZUdW5ZlXKe/PTv8/rP9vtuedTL5ucsb5y/XZMqVCXUzFUXoR1UmdjI+PD9M+6Sv14pQ98rLr6+uTrgMHpKmvLzxUA/nzeuKJJ+TEE08krzzIKocMDzILdXHBAmlavZq88qBs+ZCXHVmVX16lvD+70MZu8rNN9Jj0iR1PyIlTThx0bFrOz5YpFepiKvqyDVNDEfmRl512sTlw4ABdbYw0p8cff5y8DMjKh7poR9nyIS87svIhLzuOTX0oW6loWAMAAAAAUAC6gleTch5LthZlGiM329i5hvFzAQAAqlb6kId6zGQYBhEoFzSsgeGUaZzcbGPqAgBQaTihjxzM91dnurjABSNUGLqCAwAAAABQABrWAAAAAAAUgK7gRaSP5d/fv5+ho4zIyy4ej0tTU1OYIj/NaebMmcObV7Z75SvsPvoRySqXCuvqR10sz7JV0Pi5mW7FKcG9nSOWV5msb0XvtyoMedlxbOpD2UpFw7qItBK297dTGY3Iy07HBhy3dm2pF6Oi8po3b97w/0fZ7pevoPvoRyyraqqLY8fqN6VelLI3UmWroPFzy+hk14jklW19K+wEF/stH/Ky49jUh7KVitMLRaRj3jXHmxn7zoi87Pr6+mTdunVhivzIy46sfDSnfR0d5GVA2fIhLzuy8iEvO45NfShbqWhYFxlnuHzIy2706NGlXoSKQl52ZOVDlzc7ypYPedmRlQ952XFs6kPZOoKjgyJXxL39e6mQI5SXdvOrpa42c+bMCVPkR152ZOWjOY056ijyMqBs+ZCXHVn5kJcdx/I+lK1UNKyLqF7qZVJ8UpgiP/Ky6+3tldWrV4cp8iMvO7Ly0Zz27NlDXhVQtszj5xb9P15RkXlVErIa2bwqrS4VgmNTH+piKhrWRRSXuDTFm8IU+ZGXXSKRkJ07d4Yp8iMvO7Ly0Zy6e3rIy4Cy5UNedmTlQ152HJv6ULZSUWoAAAAAACgA/RyAKlTQmK5D1Hxt5rGb9f8d2zjWPEQOhsY0tFAWC664QmTx4qIvEwAAQK2gYQ1UmYLGdC1Qy9TB4zdrYz799eFs4ANALSrZfbAAgICu4AAAAAAAFICGNQAAAAAABaAreBH1Sq+097XLFJlS0P2omX6X0r23OfPfSkdH5t+1t1d8XrVOxwdsaWlhnEAj8rLTjMaNHUtWRuRlRz30IS87svIhr5E7Nh2J2+7KCWUrFQ3rIuqXftmf2B+mw34/asvgv5W2tsGv62tVklcti8fjMmvWrFIvRsUgL19WTU1N+k2pF6UikJcd9bCC8irBeMGFoGz5kJcdx6Y+lK1UHBkUUYM0yLF1x4Yp8iMvu97eXrn33nvDFPmRl51m1N7eTlZG5GVHPfQhLzuy8iEvO45NfShbqWhYF7n7yJ7+PWGK/MjLd0Zw7ty5YYr8yMtOMxozZgxZGZGXHfXQh7zsyMqHvOw4NvWhbKWiK3iB0rtp6z3S67avK9nyVJKEJKQr0RWmyE13WFOmcC+6FXn5smpsbLR1bS635zu0to54F1ZXXjWOeuhDXnZk5UNeZXhsmuvzdOzYIX2eluL+bspWKhrWRRwvuKenR8644Qz5wwf+IA0NdCHJp1EaZVrdtDDtlu5SL05Z07K1fPlyOe+88yhbBuTly2rv7t0yvqfHllUVPN+h4LL1r/8q51nzqmHUQx/ysiMrH/Iq02PTKvg8pWyl4pR7kSUSXH31iMcoglbcv+JDXnb97LdcKFt2ZOVDXnZk5UNedhyb+lC2jqDkAAAAAABQALqC16JMXUr0no4y7mqCkZNpeDd9dkDGYd8Aq0Luzy703m72eQCALJKPb5riTTIpPknW71gvXf1dUo6ar838majHapl+l37rKoYPDetak+3hByV4CBBGluWBFtl2vqV4IAaqUCH3kw31b9nnAQAcz0s6/auny+YPbS7re4ZbprZkPEGQ/joXRUYWXcEBAAAAACgAV6wBAOWP7twAgDKSqdt1tu7YZd8lm15cRUHDuojq6+ul+ejmMEV+PdIj23u3y2SZXOpFKXtaplpbWylbRuTl3G81N0tdOWdVRt25KVt2ZOVDXnZk5UNew3csn6nrdbZu2tWIspWKruDDMFA6bBKSkD7pC1PkN3r06FIvQkUhLzv2Wz6ULTuy8iEvO7LyIS87PhN9KFtHUHKKPI7b7r/uZjw3o0ZplOn108MUuWmZWrZsGWXLiLyc+63d7LesKFt2ZOVDXnZk5UNedhzL+1C2UnHdvoi0G8TEYybSHSKLTN1gDnQfkKe3P13UYQWG/T6WEnU/Xbx4MWXLSHN65Ve+IrFXvarUi1IZ+62JEyU2EmWrCu6Tpi7akZUPedmRlQ952XEs76M5feWvX5FX1XO8FfIo9QapNv39/VJrhjqMUyKRkLP/62y5/133SywWK8qwAtHr1aizs1PGjRtXkv+7Eofa0rpYV+qFqBAjklUZ3ScdFPB/lrIuVhqy8iEvO7LyIS+7WjyWLwR5HUFX8CLSbhDtf2unO4QRedlpRitWrCArT9lqp2xZkJUPddGOrHzIy46sfMjLjmNTH/JKRcMaAAAAAIAC0BUcANIxniMAAKg2zZmfTRSed5Lpd0m3cWV6rlFTvEkmxSfJzOtnSld/V2WN3T0MaFgX2eef//lSLwIAAACAIaj6Y/mWwc8nCg8RTX89w4NF059tpCP7bNu1TU6dcqp0S3dNPPMoF7qCAwAAAABQABrWAAAAAAAUgK7gReYZ9y5TFwkdm9nUdaIKxoNV+YbZKrlM95tkuw8l05BC6dujgG3EmIo+8ZEoW5VYD9PLblOTxI85RmTmTJGuLn+ZroR1LjLqoh1Z+ZCXHVn5kNfIHMubj+ML/DwtqA1RZP0JhtuKUMuKqKGhQZYsWWKaN9vN/K1LW/OPGVxu48EWkNfEiRPDtKxluuck2/0p+bbTELeRp2zhSNmS4Sxb2ephJUgqv5rQxELKdAXuewpBXbQjKx/ysiMrH/IauWN503F8gZ+nBbUhikzvq97Wt02myJQR/X/LFV3BizxA+o4dOxgo3Uhz6u7uJi8DypYPZcuuPxaT7qamMEV+1EU7svIhLzuy8iEvO7LyiUlMmmJNYQoa1kWllXDDhg1URiPNaf/+/eRlQNnyoWzZ9dfXy/4JE8IU+VEX7cjKh7zsyMqHvOzIyqde6mVCfEKYgq7gRb8n49xzzy31YlRUXs3Nzdz3Y0DZGlrZkjIvWwXdm1Uk9T090vzssyLHHTei/2+loi7akZUPedmRlQ952dVEVkV8VkqP9Mizfc/KccIxhCrvo84Ko2e3tmzZIjNmzJB4vMI6A5Tg/kjNq6urK0wrLq8RVtFlq1Rdwbu6pLGMy1ZB92YVUX88Lt1jxkhjPC7xUpyhr7B7s6mLdmTlQ152ZOVDXrWR1XDf251JXOIyJjYmTPuFq/yVVWLKXF9fn7S1tYUp8tOcOjo6yMuAsuWjOe2jbJn01dfLvubmMEV+1EU7svIhLzuy8iEvO7Ly0S7gzXXNdAU/jIY1AAAAAAAF4PQCAFQY7TKeTu/NzvR6xu5hyfdRNTWJTJoksn595nGsAQAAMjwbpineJJPik2T9jvXS1c8xhLthfffdd8v1118vv/71rwde0/tk3/SmN8mHP/xhOeecczL+nd6vcMcdd8j48ePD/BdffHFhS16lRvr+ylL7/PM/LzWlwu4nReXsJ4Y8dmZPj8jpp4ts3jz0cb8p1wAAFK5Un6eG/zfTs2F6enrk9K+eLps/tDmMAV7rXA3rW2+9VZ566qlwX2xk27ZtsnTpUjl48GDOv/3c5z4n3/jGNyQWi8mqVavkBz/4gbz1rW8d+pIDAAAAAFBpDesLLrhAdu/eLbfffvvAa+PGjZPLL79cHn/88ax/98gjj0h3d3doVKuXvOQl8vGPfzxrw1ob6ckN9b179w6cFdGvdPrUvrq6uoEHDej3vb29kkgkMr6//l7/RufRqX5let9INByUzp/8fbrk99CnCuqXzq/TbA9B0Ex0nuRl1++zjZ8XrWvyskfrWpdISF/aekTrqsuWa9kzraueebIse/K6WpY9/f2yZR8t+6jYqDDwfEIS0iiNR/5/iaX8rI/813m0W0q07XOta7R+ObeTdpNtbJSYztPbK326TOH/lvB9f11d6ryH1yXXdsq1rtm2U5RRNB3J7WStT57tFK2LpUyatlPauib/fij1KZ6hLo1EfVKF1Keh7PfSy1amZR/u7TTU/V4ptlP0PjpPlM1w1Kdifz6VYjtZ9/Hl+PlUiu2UXBdLUZ+G+vlU6uOIWqlPhWynTPv5aq9PQ91O0fvo75PXw7Pfq9T6VMh2yrS96qukPmWbp+CGtTaitWGd/lo+a9eulalTp6YEt3nz5pSgk11zzTVy1VVXDXp9+fLlctRRRw16febMmTJv3jxZv369jB49WubMmSMPPvig7Ny5M+PytLS0yKxZs2TlypUyd+5cmTJlSnjvbBu7tbU1vO+yZctk8eLF0tnZKSuydJnQDa1fu3btCgPM61h42g1enzCYyeTJk2XBggXyxBNPhPeN1kPzyWT27NkD6zd9+vSB9di3b58s2L1bVi9bljL/mWeeObB+CxcuHFiPbKL1014FS5YsCeuxZs2ajPPqto/Wb+vWrQPrke0kS/p2ev7znx/Kwl133ZVzO/3bc/5NHux5UDb1bpJ3T3h3aMCpK3ZdIZccfcnA/N/d+13p6O+Qm0+6OWzLXNtJy120fjm30803h8nkTZtkwc9+Jk/Mny/T//IX0VK/vrVVNs+dmzr/4WxzbadMrNspymokt5O1Pnm2k578sNQn83ZKq09/+ctfZGo8Hupizvr085/LnJ//XB786Edl+tq1MmvlSll59dVySoa6pM780pdkyqpVw1af1FC3kxrqfm9BUtkayn5vqNup0P1e1u00jPu9sWPHhuXXbbR9+/Zhq0/D8fk00ttJv/TgJNs+vpw/n0q5nfT1UtSnQj+fRnI7PfbYY+F4UPfxtVKfirGdkutirdQn73aaNGlS2FZ6HKEX+bzbSVVafSpkO0W5Z9rPt1ZJfTrttNPEKpbwNMNFQldwvT/6vvvuS3ldX9OvTPdYf+ELXwgrndxY1o2pK5fc4M51xVrHk9Ow9R7tajozVtQr1osWSV9awa6GM2NTvzhV/u7Yvxt0JXTt9rUyf+r8QVesN+7YKNv+dVtxttPMmSKnnpp6xfoPf5BYS8vgK9b68KfDFbqarggMx3Zat32d7PjIjmGtT/GXv1xi992Xe12nTZO6uXOlt74+jOGsX70NDRJ/6CHpmz9/8Lo+9JDEd+8etu206L8XyT3/cM+QttMrvv8Kuettdw1pv5dp31Ep+71qrU/V9vmUa13ZToOxndhObCe2U/q6sp1Ks50OHDggRx99tOzZsydjO3TEnwquLf70syW68npWKJNRo0aFr0yB57oxXkOIZLoSni55HssN98nzZJpf10nPnJx44okDG0RFGzCX5GXX75N/zrfsA9/HYhLPsh75lj3b/JZlT57HsuzR7zWvP//5zwN5ZXMwcTA01lS3dA+8rq8l/xzRpxJG3WrzrWvysmdcV31KcveR/6MuqYLr98k/h3nT/r+M2ymHbNspvWyN5HayLrt3O3nKZN7tlMGBzk5p0m2Ua10P7+D1pEmkXj8kEgmJJ233AYd32OVYn4a639OypSc+mw5/6GRb9vTv8y37iO33chiO7aR5bdy4MdTFaBlGYjtZl72ctpN1H19N9cm67JnmT97P10p9Gup2UlFWtVKf0r/3LHu2Y4hqrk9D3U7J+/ho+au9PhWynfT/yVa2qqU+RW2KshnH+owzzki5NK+tf70CbVmhSqMHqLAjLzuy8sl21hKDkZUPddGOrHzIy46sfMjLjqx8yOuIYWvZalfub37zm/KP//iPoW+6tvb1AWaNjY2yevVqufTSS6Xa6FkS7eMPG/KqvqzSxzdUHd0dGV+3MA8hlSGvcWPH6jdD+n9rCVlVZ10sB2TlQ152ZOVDXnZk5UNeBVyx1pvZdWgtveSvN3NH7r//fnnooYfktttuCw9zUTq9+uqrB36+9tpr5Wtf+1r4e+1i8fa3v12qjXYfWbduXdb7AZCKvKorKx3fMNPXy2a+LOPrw0lz2tfRUdZ5lcs49pWaValUQl0sF2TlQ152ZOVDXnZk5UNeBTSs9QlsV155ZXjS3HnnnTfw+tlnnx2e4HbDDTcMPIxMn7amY1zrVGnX74985CNy0UUXyXvf+16p1u6U2uWdbpU25GVHVj6aU1dXF3kZkJUPddGOrHzIy46sfMjLjqx8yKsE91gDAAAAAFCtqu/pYQAqTvO1zRlf1/uzs/1uoDt5c4bfNzXpYJSHhkvTp7UP+uOkruiZxkjs6Mj8OgAAAJABDWsAZXHfcMvUlkGv6UPPsr2e+sdp8zQ2imzbFsYgTx4u7dAft2VuYCdrbRVZMfL3OwMAAKAy0RUcAAAAAIAC0LAGAAAAAKAANKyLKB6Py+zZs8MU+ZGXHVn5xPv65Ki9e8MUuWmZ2n3rrZQtI+qiHVn5kJcdWfmQlx1Z+ZBXKu6xLvIg6XPmzCn1YlQM8rIjK5+6vj4Zs3evDrBY6kUpe5QtH/KyIysf8rIjKx/ysiMrH/JKxemFIurt7ZXVq1eHKfIjLzuy8umtr5c9kyaFKXKjbPmQlx1Z+ZCXHVn5kJcdWfmQVyqOOotIu0FMnz6d7hDDkNegp0AfHoop0+vViLLlE+/vl1GdnWGK3ChbPuRlR1Y+5GVHVj7kZUdWPuSVioZ1EWmhmjVrVqkXo+ryGhivOE3r0lbzUE6VjrLlow3qpv37RWqoYT3UukDZ8iEvO7LyIS87svIhLzuy8iGvVJxeKCLtBnHvvffSHcKIvOzIyqe3oUHajz02TJEbZcuHvOzIyoe87MjKh7zsyMqHvFLRsC6iRCIh+/btC1PkR152ZOWTiMVCo1qnyI2y5UNedmTlQ152ZOVDXnZk5UNeqWhYAwAAAABQABrWAAAAAAAUgIY1AAAAAAAFoGENAAAAAEABaFgDAAAAAFAAGtZFVFdXJ2eeeWaYYvjzqpUxrBVly6eup0cm7NoVpsiNsuVDXnZk5UNedmTlQ152ZOVDXqnq035GgYOkT5kypdSLUTHIy46sfOKJhDR2dek4EKVelLJH2fIhLzuy8iEvO7LyIS87svIhr1RcsS6inp4euf3228MU+ZGXHVn59DQ2yu5p08IUuVG2fMjLjqx8yMuOrHzIy46sfMgrFQ3rIqqvr5eFCxeGKfIjLzuy8qnXruA7doQpcqNs+ZCXHVn5kJcdWfmQlx1Z+ZBXKhrWRTZ69OhSL0JFIS87snJIJCTe10dXcCPKlg952ZGVD3nZkZUPedmRlQ95HUHDuoh6e3tl2bJlYYr8yMuOrHx6tSv49OlhitwoWz7kZUdWPuRlR1Y+5GVHVj7klYqGNQAAAAAABaBhjZrUurS11IsAAAAAoErQsAYAAAAAoAA0rAEAAAAAKAANawAAAAAACsCgY6h6zdc2D3qto7sj4+uq/bL2EVgqAAAAANWChnUR6eDoixcvZpD0MsyrZWpLys9t29sGvRa9Xo4oWz713d0ycetWiU2aVOpFKXuULR/ysiMrH/KyIysf8rIjKx/ySkVX8CLr7Ows9SJUFPKyIyuHWEz66+rCFPlRtnzIy46sfMjLjqx8yMuOrHzI6wga1kWkg6OvWrWKQdKNyMuOrHx6Gxpkz5QpYYrcKFs+5GVHVj7kZUdWPuRlR1Y+5JWK6/ZF1NDQIEuWLCn1YlQM8rIjK58G7Qq+bZvIlCmlXpSyR9nyIS87svIhLzuy8iEvO7LyIa9UXLEuov7+ftmxY0eYIj/ysiMrn/5YTLqbmsIUuVG2fMjLjqx8yMuOrHzIy46sfMgrFQ3rIurr65M1a9aEKfIjLzuy8unTruCTJoUpcqNs+ZCXHVn5kJcdWfmQlx1Z+ZBXKhrWAAAAAAAUgIY1AAAAAAAFoGENAAAAAEABaFgDAAAAAFAAGtYAAAAAABSAhnURxWIxGTduXJiWxIoVUklKnlcFISufWCIh9T09YVoLdakQlC0f8rIjKx/ysiMrH/KyIysf8kpVn/YzClBfXy/nnntuqRejYpCXHVn5aKO6+dlnRY47rtSLUvYoWz7kZUdWPuRlR1Y+5GVHVj7klYor1kWkg6Nv2rSJQdKNyMuOrHz643HpGjMmTJEbZcuHvOzIyoe87MjKh7zsyMqHvFJx1FlEWqi2bt1K4TIiLzuy8tEG9cHRo2lYG1C2fMjLjqx8yMuOrHzIy46sfMgrFV3Bi9wdYsGCBaVejIpBXnZk5VPf2ysTdu0SOf74Ui9K2aNs+ZCXHVn5kJcdWfmQlx1Z+ZBXKi7nFFFfX59s3LgxTJEfedmRlU9fXZ3sHz8+TJEbZcuHvOzIyoe87MjKh7zsyMqHvFLRsC4i7Qbx+OOP0x3CiLzsyMqnv65ODowfH6bIjbLlQ152ZOVDXnZk5UNedmTlQ16p6AqOmtC2vS3l547ujkGvZf/jtPk6Oga/BgAAAKBm0bBG1Wu/rH3Qa61LW2XFRYaxitsH/620ttbUOMcAAAAAcqMrOAAAAAAABaBhDQAAAABAAWhYAwAAAABQABrWRRSPx2XmzJlhiirOqwT3V1dsViUS7+uTpv37wxS5UbZ8yMuOrHzIy46sfMjLjqx8yCsVDy8rorq6Opk3b16pF6NikJcdWfnU9fXJOH3wHA3rvChbPuRlR1Y+5GVHVj7kZUdWPuSVitMLRaSDo69bt45B0o3Iy46sfPrq6mRfc3OYIjfKlg952ZGVD3nZkZUPedmRlQ95paJhXWSjR48u9SJUFPKyIysfuoHbUbZ8yMuOrHzIy46sfMjLjqx8yOsIuoIXuTvEnDlzSr0YFYO87MjK3xV8zN69dAU3oGz5kJcdWfmQlx1Z+ZCXHVn5kFcqrlgXUW9vr6xevTpMkR952ZGVT299veyZNClMkRtly4e87MjKh7zsyMqHvOzIyoe8UtGwLqJEIiE7d+4MU+RHXnZk5ZOIx6W7qSlMkRtly4e87MjKh7zsyMqHvOzIyoe8UnE5B0Dla2tL/bmpSWTSJJH160W6ukq1VAAAAKgRNKwBVDYdVitdT4+sXrZMFi9eLNLQUIqlAgAAQA2hnyQAAAAAAAWgYQ0AAAAAQAFoWAMAAAAAUAAa1kUey62lpSVMkR952ZGVD3nZkZUPedmRlQ952ZGVD3nZkZUPeaXi4WVFFI/HZdasWaVejIpBXnZk5UNedmTlQ152ZOVDXnZk5UNedmTlQ16puGJdRDo4+r333ssg6UbkZUdWPuRlR1Y+5GVHVj7kZUdWPuRlR1Y+5FXgFeu7775brr/+evn1r3898NpPfvIT6evrk66uLnnRi14kL3jBCwb93YYNG+See+6RyZMny8MPPyyf+tSnZPz48VJtZ23mzp0bpsiPvOzIyoe87MjKh7zsyMqHvOzIyoe87MjKh7wKaFjfeuut8tRTT0lHR8fAa/fff790d3fL29/+9vDzJZdcIl/72tcGBfyZz3wm/H0sFpPjjjtOrrvuOrnqqqukmug6T5kypdSLUTHIy46sfMjLjqx8yMuOrHzIy46sfMjLjqx8yCuV6/TCBRdcIG94wxtSXvvqV78qL3vZywZ+1ivSd9xxx6C/3bJlS2hUq2OOOaYquwz09PTI7bffHqbIj7zsyMqHvOzIyoe87MjKh7zsyMqHvOzIyoe8CrhiPW7cONm9e3fKa2vXrpWpU6cO/Kzf62tLlixJme+8886TD33oQ+HK9aOPPiof/ehHs/4/Bw8eDF+RvXv3hqlutEwbTs+W6NPotDu60u+14Z5IJDK+v/5e/0bn0al+5SoQ9fWHYtL5k79Pp+8Rvd7f3x++dH6dRsuWTk826DzJy67f699kEq1r8rJb1lWXLdeyZ1rXhoYG07Inr6tl2ZPfT/+fbNkP13ZSif5ERW2nqGxF6z+S22kk6lNTvEkapXHwsktsYFov9dIjPRKXeJg/0/+RvJ2i/GqlPg11O6WXrUzLblnXaP0qoT4Vsp10Hv3SeaJsyq0+lct2su7jq6k+WZY927om18VaqU+FbKdoGWqlPhWynTLt56u9Pg11O0X7eP198npUe30qZDtl28/XV0l9yjbPsDwVfMeOHTJq1KiBn/X77du3D5rviiuukP/4j/+Qq6++Oly9bm1tzfqe11xzTcZu4suXL5ejjjpq0OszZ86UefPmyfr162X06NEyZ84cefDBB2Xnzp0Z318fC69PsFu5cmW4L0C7MOh7Z9vYuqz6vsuWLZPFixdLZ2enrFixQnLZtWtXuK/83HPPDevb1taWcT69wr9gwQJ54oknwvtG67F58+aM88+ePXtg/aZPnz6wHvv27cs4/5lnnjmwfgsXLhxYj2yi9Vu1alU4OaLrsWbNmqwnWqL127p168B6PP744xnnT99OJ5xwQnj9rrvuGvHt9Nf2v1bkdoqyGsntNBL16eaTbs447yd3fvLQMtXPlLNGnyXf2/c9ObnxZPngSR/MmE+0nZ588smB12qlPhW6ndLroac+6YdbtH6VVJ+Gsp3Gjh0bprqN9LOuHOtTuWynadOm5dzHV3N9KmQ76eu1Up+Gup30Ak2kVupTMbZTcl2slfrk3U6TJk0KUz2O0Ftda6E+FbKdotwz7edbq6Q+nXbaaWIVS3ia4SLhHuuLL75Y7rvvvoGNohtMg1M33XRTONhIbxhrF/LvfOc7MmbMmFAgPvjBD4aVi7qH57tiPWPGjBB2pgeelcuZMX0PLVhaePT/KLczLuV2Zkzn08q2aNGi8H+N5BnMRd9fJPe9876KumKtZSvKqhLOYHq207QvTZNTp5w6aN6129fKaVNPG3TF+rEdj8nmD23Ouq76IMU777wz1EVdnlqoT4VcsU4uW9V2pnk4rljrB63eGqX/TznWp3LZTjqvZR9fTfWp0CvWUV3Uiwi1UJ+Gup2S9/H6HrVQnwq9Yp2+n6/2+lTIFWvdx59//vnh72qhPhWynQ4cOJDxGKKa6pOu49FHHy179uzJ++Dtgq9Yn3HGGeHsgJ4JUNqonj9/fso8eubj6aefDo3q6CyArrh2K4/ODCXTq97JV8GTA8/04RxJHpw82oC5JM+T630zzZNv/mijpX9vWXb9Pt9A68nLbllXz7Inz2NZ9uR5LMse/T4q/Pm263Bsp1g8VpHbKT2rkdhO1mUvZDt19XdJt3QPmichiYGpNqpVv/SH+a37glqpT4Vup2z1kP1eZvr7aBnKrT6Vy3aKDnjy7eOrsT7lW/Zc82dav1qoT5W2nSKVtJ0y1UW2U2bJ60d9yr+dcu3nGyq8PmW6CJxNwc9G1/um9cxORBvWegZRrzjfeOONYaoPK9NL7dqlQulr2p0uU6MaAAAAAIBK4rpirVeely5dGvqza2NaH0h29tlnh+7h3/ve9+Rvf/ubXHrppeHsgjaw9X7qCy+8MPRj1yG4Pv3pT4c++Tr/zTdnvqcSAAAAAIBK4r7HuhT0HusJEyaY+raXkkapV+b14QGebgO1qpR5tS5tlRUX5X4AXTmp9rLVfG2ztExtGfR62/a2rK+3X9Zes3kVE1n5kJcdWfmQlx1Z+ZCXHVn51EJeex3t0IK7giNV9BA32JCXHVn5kJcdWfmQlx1Z+ZCXHVn5kJcdWfmQ1xE0rItIny6nT0DN9cQ7HEFedmTlQ152ZOVDXnZk5UNedmTlQ152ZOVDXqnoCl5EGmX0SPlq7Q5RLXlVYlfwai5bw9EVvJrzKiay8iEvO7LyIS87svIhLzuy8qmFvPbSFbx0dJBy2JGXHVn5kJcdWfmQlx1Z+ZCXHVn5kJcdWfmQ1xE0rItIz9isWLGC7hBG5GVHVj7kZUdWPuRlR1Y+5GVHVj7kZUdWPuSVioY1AAAAAAAFoGENAAAAAEABaFgDAAAAAFAAGtYAAAAAABSAhjUAAAAAAAWgYV1kOo4b7MjLjqx8yMuOrHzIy46sfMjLjqx8yMuOrHzI64hYQkf2rqKBuQGL1qWtsuKiFaVeDBzWfG2ztExtGfR62/a2rK+3X9Y+QksHAACAWrTX0Q7linUR9ff3y44dO8IU+ZGXHVn5kJcdWfmQlx1Z+ZCXHVn5kJcdWfmQVyoa1kWkhWrDhg0ULiPysquFrPQqdPpXR3dHxtfzqYW8ioWsfMjLjqx8yMuOrHzIy46sfMgrFV3BUZPoCl4Z2E4AAAAoFbqCl4ierdm0aRNnbYzIy46sfMjLjqx8yMuOrHzIy46sfMjLjqx8yCsVDesi6uvrk7a2tjBFfuRlR1Y+5GVHVj7kZUdWPuRlR1Y+5GVHVj7klYqGNQAAAAAABaBhjZrEfbuVge0EAACASkDDGgAAAACAAtCwBgAAAACgADSsAQAAAAAoAA3rIorFYjJ58uQwRX7kZUdWPuRlR1Y+5GVHVj7kZUdWPuRlR1Y+5JUqlkgkElJFA3MDAAAAADCS7VCuWBeRjuG2ceNGxnIzIi87svIhLzuy8iEvO7LyIS87svIhLzuy8iGvVDSsi6yzs7PUi1BRyMuOrHzIy46sfMjLjqx8yMuOrHzIy46sfMjrCLqCAwAAAACQhq7gJaLdINatW0d3CCPysiMrH/KyIysf8rIjKx/ysiMrH/KyIysf8kpFw7qI+vv7ZfPmzWGK/MjLjqx8yMuOrHzIy46sfMjLjqx8yMuOrHzIKxUNawAAAAAACkDDGgAAAACAAtCwBgAAAACgADSsAQAAAAAoQL1UgGhEMH3ceTnr6emRAwcOhOVsaGgo9eKUPfKyIysf8rIjKx/ysiMrH/KyIysf8rIjK59ayGvv4fanZYTqimhY79u3L0xnzJhR6kUBAAAAANSQffv2hfGsc4klLM3vEtNHuG/btk3GjRsnsVhMyvmMhjb+t2zZkncAcZCXB1n5kJcdWfmQlx1Z+ZCXHVn5kJcdWfnUQl6JRCI0qqdNmybxeLzyr1jrShx//PFSKbRgVWvhGg7kZUdWPuRlR1Y+5GVHVj7kZUdWPuRlR1Y+1Z7XhDxXqiM8vAwAAAAAgALQsAYAAAAAoAA0rIto1KhRcuWVV4Yp8iMvO7LyIS87svIhLzuy8iEvO7LyIS87svIhrwp8eBkAAAAAAOWKK9YAAAAAABSAhjUAAAAAAAWgYQ0AAAAAQAFoWAMAAAAAUAAa1gAAAAAAFICGNYCaxsAIAAAAKBQN6xIf0HNQb0dW2R08eFB27NhR6sWoSLFYrNSLUBG2bNkSptRDoDz09/eXehHK3urVq8OU/RaAkUDDeoQ/ALdu3So///nPZd26deGAnoP6zKIPwT179sjvfvc72bhxI1nlsGvXLvnLX/4y6HUOJrLXxccee0x++tOfyjPPPDNoHnJLzeqjH/2oXHLJJbJ3717qocH//u//yp///OeUckSZQiGi8vP000/L//zP/4QyFo+nHsJRxlL3Wx//+Mflc5/7XDiOYL+Vnx6X3nPPPdLV1TXwGidvUKiNGzfK0qVL5ZFHHhm0r6rGfRYN6xGgBUc/ALVR/apXvUpuu+02Offcc+U973lPVRaqYtAPQf0w/OQnPxl29K9+9avl3e9+t9x///0D85DdEXV1dXLvvfcOep2DicG0LurB6Wte8xq57777ZP369eFAQhvY27ZtCwcS5HZkv7V58+ZwAuKtb32r/PCHP5Tt27cP/B6Dafm5/fbb5corrwz7rujEzV//+lcOUtPoPl4biMhP90n79u2T733ve6FMXXTRRbJ8+fJB89S65P3Wj3/8Y3nXu94l3/72t8N+XlEHs9u0aZPccMMN8oMf/ED+8Ic/hNd2794dMmN/n7rf0rq3c+fOUi9K2du3b5/ceeed4Rjrsssuk1//+tcD+yotU9W4z6JhPQKignP99dfLP/zDP4QPRu1WqTt/PZMToStvqhUrVsg73/lO+dSnPiVr166VqVOnhsb15z//+fD7aqyQQ3XgwIHQKFR6QKEnb84880y56qqr5PHHHy/14pWd//qv/5L3ve998rWvfS0c2L/3ve+Vl7zkJaEx9N///d+lXryy8q//+q/yjne8Q97ylrfIi170ooF9FvUvM92vv/KVrwx1UuuhHtT/8Y9/lM985jPhwBVH3HHHHeFks9ZDPYBHbnpi+e///u/ln//5n8OBvZ5QfeCBB+S6666T73znO5SvJB/+8IfD8dYb3vAGef3rXy/PPvtseD39Kj+OeNnLXiaTJ08ODcZf/vKX4YTqpZdeGnrfsL8/QntR6rGV7tv12FQbj8hs1apVcvbZZ4djLP08bGxslJ/97Gfy2c9+NhxvRSdwqunEDXuYEaAFRs/4acNQr1KrsWPHhgMKvaLR29s7UFmjQlbLorx0Z645qfHjx8vVV18dusBpZt/97ndLvZhl5XnPe17ITK9a79+/X57znOfIV7/6Vfl//+//hZMTemUWR8rXCSecEHJRekD/ute9Lhy06oHFXXfdJWvWrJFapwdSy5YtC/VNu1OqmTNnyqOPPipf//rXw1l7ZKblS0/caANozJgx8qEPfSjsz5qamlK6WdY6bfScd9554eBUr8Deeuut4XkRSBUddOrJ9/r6+rCv17Kkt5VpA1t7kehVbD2GqHW639LPQc0iOgl/zDHHhB6DX/ziFwdOQGNwGZs0aZIsWbJEFi9eLC984QvDyRptZOuX3m6GQ84//3yZP39+uDXqpptuCj259AIG+67B2tvbQw9BPfnQ19cXrl5rl/Cjjz5anvvc58pDDz0U5qumEzexRDWdJihzWqC6u7vDjis6a6pXx/TqhjYc9YrQKaecEro/Q8KVfe3KpVf6n//854cdu37pwcV//ud/yje/+U0ZNWpUVVXIoYi60+hBqXbh+tKXvhQO7CN6llB3ZHrFDIc88cQT4WqPnpDQkw/HHXdcOFCN6qm+pg2jWqdZ6EHqF77whfChqAf1Sk9CaGYnnXRSqRexbK1cuVLGjRsnp512WjgQmzFjRjjA15/1pOqECROklmnjULvH/+1vfwv7d+0i+I1vfEMmTpwYTkAvWLAgHLjqZyMO0RN+erJLP/d0H6aNnw984APhd/rz97///ZDd9OnTpdavkunJ5Msvvzz8HO23HnzwwdD4WbhwYYmXsHy1tbWFk/NnnHFGuNKvDUilVxr1diA9aV/L9HNQj+P1RJYeP2iPkZ/85Cdhf3bOOedIa2tr+P2xxx5L7wiRcCylx/HaY0S/XvrSl4YTzkpP2OhJem37VNOxBA3rEdbZ2RkO4Ht6esKOSg9c9cNRDyD07L0esOoVDhyijcRrrrkmZKMH8qeffno4EaFnovUKhx6s1jrd0WsjUbuefvCDHww7eO1aedRRRw3Mc+2118rHPvaxmt3R57qXR3uMaGaaoX7pgZce4OsBa62ftEmn2ehBvT4oT7sJzp07N1zdwCFajpLr2N133x3OyGtDR7ul6okvPRDT3iTRwX4tsdxTp/subWTrvl2vaHziE59gv5V2wkbLjtZDvRKkB6p6pVHpfktvl6JsZf6M1Ktm2gjSRs+FF14otS7KLMon+TY8LWda//Q2IO21pPsxfYhlrZctPXZvaGgYNI9e2NCT0Ho8oT0t9dg1OdNazurJJ58MP2s2+oC8F7zgBeEiotLu9PrZWE3liob1MNN49eBKd1xaoE488UT5P//n/wz8Xq8k6s5eK+GiRYvk/e9/v9Sy5Lw0Gz2zrGdO9Wz8rFmzwskIraj6hMFp06aFzHBkZ6ZnAP/xH/8xnLH/9Kc/LbNnzw47Nb3H5dRTT5Vapjv1L3/5y6HXw+jRo+XNb36znHzyyYPm09sNdMev2dVyedIu8dpQ1NtT9Irim970ppR5Ojo6wr1SekVjzpw5JVvWci1reqBw4403yq9+9atwv6Luu3IdmNVqPdSrFVp+knPRz0Q9YXPxxReHhmIti/LSfbv2gNBM9JYM/YzU8qUH79q41oaQ7ue1R0St0v2Vdo/Xz0I9OaO39kS3/ESfkbrf0kaQXtCIDu5rVXIjSL+PeiVpT0rtBq69I6I6Ge3TapXun/R5P7pv0i8dJUN71STvt/TCmdZP7ZWqJyRqPau9e/eGTPQ+fT1O0DKmJ+SfeuqpcNJGr2a/4hWvkHnz5kk1oWE9zLSBo4VLuwBqlzd94IH+rN2YtVIqvan/T3/6U/hgrHXJeWk3QX24gdInVerDuCJ6v5R+6cOUapWeedeHImn50Z1UMu0Gp/eh6w5Lr1zX8sFpdPCgQ69omdGrrHqf65QpUwa6JEW0y5L2ItGHm9Wy5Hqo91LrbQT68y233BIO4mGri3qgr10G9Yq1HmzogWmt9oKw1MPocETn27BhQ5inVmXKS3sl6dVWPVBVel+nNrr11h/tMl/rt69obxDtxqyfe3pCWZ9toDnqrSw6EgtS6TGCft5pI+jv/u7vUn6nzx7R2zK0Qa0nb2p9v6W3aOq+XG/r0dsR9d7h6Pg0eT7tzaVdxGtRpqyeffbZkFV0K6Luy7Tc6UPy9ISE9jytOtqwxvDo6upKfPe73x30+le+8pXEKaeckvjyl78cfv7Wt76VuO+++xK1LltempPmpbn19vaG17Zs2ZLYs2dPopb99Kc/TVx00UXh64Ybbkjs3bu31ItUttrb2xNvetObBn5ev3594m1ve1vit7/9bfi5r68vcfDgwcRf/vKXxIYNGxK1LN9+K6qH/f39JVm+SquLWrZgr4ednZ0lXMLKyOuBBx4o6XKVo+7u7sS3v/3tQa/fcsstiRNOOCHxsY99LOzbqI9HrF69OpSnc889N3HZZZcl/vrXvw78LjrWQiLszzWnyNNPP534p3/6p8SyZcvCz/pZyPGXLSulx1rVrDZvXBohepZPr1JrN8BkehZVn+SpXQP1rKCOs6hduGpdtrx02IwoL/29Ov7442v+oTZ6Nlm7wuvDavReOy1X2o0Zg+mZ0Re/+MVhmDu9iqhdAP/t3/5t4AmxejVW7/PRM9HaDbyWWfZb+vtavYJhrYv60EBVq/cHD7UeahdKvR0I2fOKxkbX7vRvfOMbw20btU6z0q7MP/rRj1Je11ujdEhF7T4fDXOKI/t67bqsD73T8qUPK7v55psHfodD9LlH2pVZe9FoTtoDSUep0SuxSrs7661lemtUrRuTJyvtRv/a17524POxGrGHGUba7U+H8dGHPuhDa3RIjIgOvaVPV9Tu3/TGt+Wl91hrF1WtrJCw89K89JYCvd9HxzfVA1Idd1jLVfTACBza2UcP2dIDKy1DWp6isXO1sfjwww+Hbqm1zloP2W/lrov6wB/qor8e6rM1kkc1qGX58tLbfDQvfQYCJNzbqk/+1oebajf5iGant7D8/ve/Z7+VRE/U6IkbfTCsPuBUh1X87W9/G/ZlemJ1/fr15HW4/OgJLH3wsH6v3eO1K3M0frWeZNbhFGv9nn1LVvX19SGran4WRO0+iWAE6A5Jz9bo/T56kLV58+bwvRYoPUDVD83m5mau/Djy0gMvzjgfkvz0eD0br/fe6X1S+rApPUt/2WWXcYCaJPmAVMuQPlU3ok8e1ifPp9+rXous9ZD91hHUxeLVQ324J/XwCPKy77d0/6RXYPWZB/qEdH0ono6Trhcx9IqZZsl+6wh9eGBEc9GTD7qv16dba0/KK664ouYfehqVLT35oM/9UdFD3PRzUHtJ6HMQ9OGenOAiK8XDy0aIPkxKu1FqhdTuSPogEn3C5z/90z+xo8+AvIZOG0I6zrc+KI/uXLnplUR9+ulvfvMb+d3vfkdeaaiHhaEu2lAPfcgrN72Kr7nok9R1+vKXvzx0FddRV9hv5addm3W/pUNOciEjO72FRYdO1J6C2huCephdLWVFw3oEhzPQJ3rqsFF6RUOflKdnmZPPPoO8hpqXPnFYn7CrjR49g6pPRq31e9Dz5aVjMut4k2eddVY44NJhf2pdlA/1cGjD1VAX/VlRDzMjr8JovdNnZujtPXqlUcdFx2BR2dJ9lo4Aob1t9JYD/VlvNUDmrPQ5I9pDQm8/0Pur9RktOKKWs+JU1DCLDlLVt771rXCgpd2/tYsSB6eDkZdPdOD1ta99LTygRemZQA7kc+el478+9thj4fvLL7+8pg9Ok8+tJjeqqYf2vKLvFXXRnhX1MBV5FS7KTLuCay8bvU2DRnV2mfZbepWaRvVgyfUwuodf7+ev9obiUMRqOCsa1sNAD0Ij0UHqunXrwsOATj/99JIuWzkir6FnpTQrffDWGWecUcIlq6y82traBsZA1wdN1aIoDz2bvHHjRvnpT38aumslN66ph/a8FHXRlxX18BDysovy0KvROnZ8dNCefBIiqod6Vb/W5ctLaV7afX7hwoVSyzxZRSP56D3ptYissqNhXUS/+MUvwhPwonsHtDtNdNZGh4B461vfOvA6yKuYWUVXLsjKl5fOU6uiPHQ4O316tQ7V9pGPfCQc2FMPh54XddGfVS3XQ0VedlEe//zP/xy+7rzzzvCz3qbS3d0dvtf7ON/+9rdLrddDRV7Fz+ptb3vboBP3tYascij1QNrVYseOHYklS5YkLrzwwsRPfvKTgdd14HjV2dlZwqUrP+RlR1Y+5GV3xx13JF796leH7zdt2pS4/vrrE9/4xjcGfn/gwIGU7GodedmRlQ952d11112J17/+9eH73/zmN4l3v/vdide+9rWJiRMnJq699tpSL17ZIS87srIjq8y4Yl0kOk7b61//ennlK18pt956azizvHbt2oGzOrV+X2I68rIjKx/ystMsLrroovC9PpxMh+5JHnNZu3fpEzx5ku4h5GVHVj7kZacPQtLx4pXmoaMW6L2cy5YtC93oP/3pT4er+jyb9xDysiMrO7LKjIZ1keiT73TsVx2G5pvf/Ga4p+CTn/xk6M6lT1pMvlcK5OVBVj7kZXfiiSfKX/7yl/Dk72j86tmzZ4d7o5TeX/3GN76x1ItZNsjLjqx8yMtOH0am+3n14he/WD71qU+FcYb1/vP7778/fK84CXEIedmRlR1ZZcZwW8No06ZN4cBex1E85phj5LrrrpPjjz++1ItVtsjLjqx8yCu7rVu3hoP4yPbt22X9+vVy3nnnyYUXXhiuor3hDW8o6TKWE/KyIysf8hqa9vb2MGqB3h+sT7T+9re/HU6oPu95zyv1opUl8rIjKzuyOoSGdYG0q5Z2z9JuXE1NTaEL1ymnnJIyjw77cO+998qPf/xjqXXkZUdWPuTly0pPMowePTo84O35z3++vOAFLwi/04+ElStXytNPPx26c/3sZz+TWkdedmTlQ15D28fr16xZswb28dFIBnoiQh+gtGjRIql15GVHVnZklRsN6wLo2WR96l1LS4vcddddsnfv3nDGWcd61bHa9F5PpWMD6lmc6OdaRV52ZOVDXkPPat++fWG815NPPjk8AVzvm/ryl78cxpy85557amaIjGzIy46sfMirsKz0hEPyPv6ZZ56RN73pTbJ06VJ57nOfK7WMvOzIyo6s8qNhXYDbb7895arYU089FR6SpGO6nXTSSfLOd76z1ItYVsjLjqx8yKu4WW3YsEEeeOABed/73ie1jrzsyMqHvIqbVUdHh6xatapmx/hORl52ZGVHVvnx8LICHH300eEKWOQ5z3lOuP9Jn0b88MMPy29+85uSLl+5IS87svIhr+Jk1dbWJnfccYfMnTtX3vOe95R0OcsFedmRlQ95FXcfP3bs2Jo9mE9HXnZkZUdW+dGwLoB219KzND/60Y9SXl+wYIFceeWVsnPnztoaFD0P8rIjKx/yKk5Wn/nMZ2THjh0DDx8BeXmQlQ95FW8fr1mxjz+CvOzIyo6s8mNvXQB9uvCSJUvkP/7jP+SMM86QP/zhD+F17V2vH4Q6bIY+jASHkJcdWfmQV/Gy0uEzOJA/grzsyMqHvIqXVWdnJ/v4JORlR1Z2ZJUf91gXyWc/+1n54Q9/GLpD6P0HWrC0K4R2k8Bg5GVHVj7kZUdWPuRlR1Y+5GVHVj7kZUdWdmSVGQ1rp/RuWb29vVJfXx++7+npCTfx6/i4+nqtjd2WCXnZkZUPedmRlQ952ZGVD3nZkZUPedmRlR1Z+RxKBnlpV9KjjjpqULes6Oc777wz3ND/gQ98oERLWF7Iy46sfMjLjqx8yMuOrHzIy46sfMjLjqzsyGpouHnH6Fe/+pV85CMfkYMHD4Z7CdLP4vzkJz+Rl7zkJQOv1zrysiMrH/KyIysf8rIjKx/ysiMrH/KyIys7shoaGtZ5RIXl2WeflfXr18uoUaMkFouF16LCpU/I03sK5s+fn/J6LSIvO7LyIS87svIhLzuy8iEvO7LyIS87srIjq8Jwj7WxO4Q+/U7Pzuig6DpO24oVK0JB04Kl40y+8IUvlKampnBWJyqAtYq87MjKh7zsyMqHvOzIyoe87MjKh7zsyMqOrIaOUwwGy5cvl9mzZ4fC9fTTT8s73vGOcKP+jBkzZOLEifLMM8+EwqUoXOTlQVY+5GVHVj7kZUdWPuRlR1Y+5GVHVnZkVQC9Yo38NmzYkPjlL3+ZWLx4ceKKK64YeH3r1q2JT33qU4mNGzeWdPnKDXnZkZUPedmRlQ952ZGVD3nZkZUPedmRlR1ZDQ1PBTd6wQteEL70PoKTTz45vNbX1yfTpk2TxYsXy759+0q9iGWFvOzIyoe87MjKh7zsyMqHvOzIyoe87MjKjqyGhnusM4ieerdx40Z57LHHwteZZ54pL3/5ywfNu3379nAPwkUXXSQTJkyQWkRedmTlQ152ZOVDXnZk5UNedmTlQ152ZGVHVsXDFesMoqfbfehDH5KpU6fK5MmTZenSpXLMMcfIvHnzBubr7u6Wq6++OtxzUMuFi7zsyMqHvOzIyoe87MjKh7zsyMqHvOzIyo6simiIXcir3g9/+MPE+9///vB9e3t74hvf+EbKPQa9vb1h+vDDDyf6+/sTtY687MjKh7zsyMqHvOzIyoe87MjKh7zsyMqOrIqDp4Jn8fznP19e9rKXhe+PPvpoec973pNydua+++6TW265RU499VSeiEdeLmTlQ152ZOVDXnZk5UNedmTlQ152ZGVHVsVBwzqLk046Kdykr90edKr0UfM6KLr605/+JDfccEOJl7J8kJcdWfmQlx1Z+ZCXHVn5kJcdWfmQlx1Z2ZFVkRTpyndV6e7uDtOnn3465fVt27Yl/ud//id0gViwYEHid7/7XYmWsLyQlx1Z+ZCXHVn5kJcdWfmQlx1Z+ZCXHVnZkVXx8FTwNGvWrJHf/e530tvbG27a15v4zzvvPGloaAi/v+eee0J3iF27dslNN90ktY687MjKh7zsyMqHvOzIyoe87MjKh7zsyMqOrIqLp4In+fOf/xweI3/ppZfKr371K9m0aZM89NBDsmrVKnnb294mp5xySngE/be+9S1pa2uTWkdedmTlQ152ZOVDXnZk5UNedmTlQ152ZGVHVsVHwzrJ+vXrZdasWeEszete9zrZu3evrFu3Th544AFZuXJlKGCvetWrZPbs2TJlyhSpdeRlR1Y+5GVHVj7kZUdWPuRlR1Y+5GVHVnZkVXx0BU8aGD0aFP21r32t1NXVDfz+iSeeCGdrTj755DAgeq0jLzuy8iEvO7LyIS87svIhLzuy8iEvO7KyI6vhU9NPBY/OKejT7zo6OmTLli2h+8MXv/jF8FS8yIknnihf+MIXwuPle3p6pFaRlx1Z+ZCXHVn5kJcdWfmQlx1Z+ZCXHVnZkdXwq+mGdeTKK6+Ut771rfLd735XnnzySfnFL34hkydPlq997WvS1dUVbtj/3//931DAopv5axl52ZGVD3nZkZUPedmRlQ952ZGVD3nZkZUdWQ2fmr7HWgvMgw8+GJ6Ip10e9uzZI6tXr5YXv/jFoUvExz/+8TBu29y5c8MZG72Rv5aRlx1Z+ZCXHVn5kJcdWfmQlx1Z+ZCXHVnZkdUISNS4X/7yl4lbb7114Off//73iXe9610DP999992JLVu2lGjpyg952ZGVD3nZkZUPedmRlQ952ZGVD3nZkZUdWQ2vmu8KrmdlHn/8cens7Aw/v+hFL5IzzzxTHn744fDzM888I2eddRb3GBxGXnZk5UNedmTlQ152ZOVDXnZk5UNedmRlR1bDq+Yb1s997nPlLW95i9TXH+kV/4pXvEL2798fvl++fHkY3417DA4hLzuy8iEvO7LyIS87svIhLzuy8iEvO7KyI6vhxXBbSY+dV729veHG/d///vfhfoOrrrpKVqxYUepFLCvkZUdWPuRlR1Y+5GVHVj7kZUdWPuRlR1Z2ZDV8av6KtYoKl9IzOGPHjpVHH300nNH5xCc+UdJlK0fkZUdWPuRlR1Y+5GVHVj7kZUdWPuRlR1Z2ZDV8aFhnMX/+/DAo+vnnn1/qRakI5GVHVj7kZUdWPuRlR1Y+5GVHVj7kZUdWdmRVHHQFz0Jj0Rv3GxsbS70oFYG87MjKh7zsyMqHvOzIyoe87MjKh7zsyMqOrIqDhjUAAAAAAAWgKzgAAAAAAAWgYQ0AAAAAQAFoWAMAAAAAUAAa1gAAAAAAFICGNQAAAAAABaBhDQAAAABAAWhYAwAAAABQABrWAAAAAAAUgIY1AAAAAAAydP8fUwTGTfhEgw4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制K线图\n",
    "from mpl_finance import candlestick2_ochl\n",
    "\n",
    "# 读取数据\n",
    "file_name = './000001(20250101-20250328).csv'\n",
    "df = pd.read_csv(file_name).rename(columns={'Unnamed: 0':'date'})\n",
    "df.date = pd.to_datetime(df.date)\n",
    "\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "axes = fig.add_subplot(111)\n",
    "\n",
    "# 绘制K线图\n",
    "candlestick2_ochl(ax=axes, opens=df['open'].values,closes=df['close'].values, highs=df['high'].values, lows=df['low'].values,\\\n",
    "                 width=0.75, colorup='r', colordown='g')\n",
    "\n",
    "# 设置x坐标轴，刻度为每隔5天显示\n",
    "axes.set_xticks(range(0 , len(df.index.values), 5), df['date'].dt.date[::5], rotation=60)\n",
    "axes.grid(True)\n",
    "axes.set_title('K-line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "782de1b0-6fee-48c9-99ca-95a3795d970e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5YAAAIbCAYAAACQUgHMAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgIpJREFUeJzt3Ql8FdXd+P9vQgIBhKAkgGxBEaGVKtTlQUQ2C0VwaetjrdanShdt1bZo1eqvfwtUrdraR9yqra1LXR6V2k1FiwuroKCCiFaKQlgLJEHCFsh2/6/vCXM9mdyb3P3Onfm8X6+8Jrl37s2Ze74zd75zzpyTFwqFQgIAAAAAQILyE30hAAAAAACKxBIAAAAAkBQSSwAAAABAUkgsAQAAAABJIbEEAAAAACSFxBIAAAAAkBQSSwAAAABAUkgsAQAAAABJIbEEALTQ0NAgjY2N4b9ra2sjPhcKhaS+vj7m9/Q63S7dpkj0cfszyYXtAQAgU0gsAQAtbNmyRR555BHJy8uTH/7wh7JixYrwc9u2bZPf//730r17d7nlllvkX//6V4vXayL61FNPycSJE8173HzzzfLqq69G/F833XSTXHbZZUmX+ZNPPpFf/OIX8oUvfEGOOOIIefjhh+Uvf/mL/OpXv5LrrrtOdu7c2eZ76LZcc801psznnXeevPDCC7Jjxw7z3L59++TPf/6zjBkzRsaPHy9z5syJ6XPU9/nyl7+c9PYBAOBleaFol2YBAIGnCda8efNk7Nix4ccOHjwo999/v1x88cXSo0ePVl//6KOPytSpU6O2AqrVq1dLXV2dDB8+PCVlnjFjhvm/5eXl4cd++tOfyrvvviuvvPJKTO9x0kknyeDBg+XJJ59s8Zwm2j/+8Y/lmGOOiem9nn76aXnwwQdl/vz5cWwFAAC5hRZLAEDMqqurTdJ21VVXtZlUxmro0KEpSyqjGT16tLz22mum/LG48sorTWunu5VTu79u37495qRS9erVK+7yAgCQa0gsAQAx0W6dmmxpt9X27dtLLtHWy+LiYunYsWNM63/jG9+QTp06mSTapt15J0+enKZSAgCQuwqyXQAAgPfpPZZr1qwx3VpTbcGCBfL3v/9d/vd//9f8rS2Ly5YtMy19eo/jG2+8IT/5yU/MfZKOl156SZYsWSJ9+/aVtWvXmu6pZWVlEd9/48aN8rvf/U7+9Kc/mYRYE+THH3/cdL/V1lK9D/SJJ56QrVu3mnshR44caRLQb3/72+Z1V199tekSrJ5//nm54447WiStf/zjH02r665du2T58uVy/fXXy1FHHdWiLFrm73znO6Zr7qWXXmq6x2ryqkt9nz179sh9991nBkTq1q2b/OMf/5D33nvPdL09++yzzf/WujjyyCPN/Z6HH364eV9tRb3zzjvl6KOPNq2sAwcONMkxAAAZo/dYAgAQiX5NPPDAA6FOnTqFLr744lBtbW1cr3/kkUfMe0Szbt260LBhw0KXXHKJ+XvZsmWh0tLS0KhRo0IbN240j61cuTJUVFQUOnjwoPn7o48+Cp111lnh91i/fn1owoQJ4b+nT58e6tatW+jee+8N/fKXvwwNHz489LnPfS705JNPhtd59dVXQ5///OfDf9fV1YVuvPHGZmX75JNPQvn5+WZdtX///tC0adOarbNt27ZQ//79zdLx4YcfhgYPHhyqqakxf8+bNy80ZsyY8PO6rfq5OPT5srIy87tu89ixY0MjRoww/18tWbLElOO3v/2t+buxsTH01a9+1WybQ7e/vLy82d/6uQAAkCm0WAIAWjVkyBDTonj++eebVjNtKTvssMNS8t7aqqf3ay5atMj8ffLJJ8v3vvc9KSwslH79+pnHTjjhBDMCrbbK6WM6cFD//v3NoDjOaK3u+xi126u+r7rxxhtNF1Ytu7bm6eM6quv+/fvlgw8+kOOOO84MUKQtiTZt/Zs0aZIZeOeMM84wrYf//d//3WydP/zhD2YU2p49e4Yf+9znPielpaWmfNoqGQ/dPh11VnN6/f/q1FNPNe935plnmr+19VRbWbVlVOmgRNraunTpUvOjtMVSR7MdMGBAXP8fAIBEkVgCANr0pS99ySR/mtyMGzdOXnzxxfDgPZWVlc26XWqy+NBDD8X83u3atWv2tyaVbgUFBeF5I3VaER1cx77X0Z0URir/17/+dTOFiiaWmpx961vfMqO+/vKXvzTdUCdMmNDidfp/vvKVr5gpVt5++2254IILmj2/YcMG0y3VrU+fPhGnYYmV0/XWUVRU1OJv+/P4/Oc/36wO6AYLAMg0Bu8BAMRE70fUFrEDBw7IaaedZhIaVVJSYloEnZ9Yk0pNVCsqKuIuR+/eveWtt96K+3VdunRpNnjPJZdcIs8884xp8dQWzki0xVJbEfXexkgJpLYIRtqGzZs3m+QyWiLd2NgoqaKfxzvvvBNONAEAyAYSSwBAzHSwHE0INWnSQW60G2YiNDnV+Sa1i2e8tHVSB/rRgX2UDsKjg+/s3r3b/K3dSPfu3dsseVu/fr0899xz8rOf/Sz8mHY11e35xS9+Ee5m6pafny8/+MEPzIA6F154YYvndYCflStXmm66Dh1s5z//+Y9JXCMZNGiQeY369NNPTblqampa3ebW5gEdMWKESZi1JVa79zpl0EGRAADIFLrCAgAijqQ6Z84c8/tTTz1lul5qAqM0CdIkSxMuvR9Q72E877zzZPDgweHXV1VVmcRG70tUOrpq586dTauajpyqo7J26NDB3Ceo92+uW7dOFi9ebBIoHRVWWxG1hVQTvmeffdZ0RdV7Ha+99lpTDn0/HSn1u9/9rpx44okyc+ZM011WH/+///s/8/+1a6t2gdURVrV1VadK0fsVbZr86Yis2poZjSaP2kIaqcVS7+3U8uqIrM5cnJo0asuutoLq6LaPPfaY+f/aOqrdcS+//HLTvVYTW71X8pxzzjGfwz333GPu5fznP/9p3kc/22HDhpnuu5s2bZLf//73JnnU+0Sdz+T9998393hqkq0Jt5ZH7/HU+tHPBwCATMnTEXwy9t8AADmhtrbWtNY53Ta1VdC5z09/168OTeT0XkBN3HQdTRTjdfDgQXNPpb6P/k/nd/3R99QypNOqVatMV1ZN6LxAW3Kdz10/G/1MdamfvX4++rh+NrqOfj762bvvvwQAIBtILAEAgeW0IroHywEAAPGhKywAIHD0fkwdzVaXJJUAACSPxBIAEDjTpk0z3WD1/kgAAJA8usICAAAAAJLCdCMAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABISkFyLw+2xsZG2bp1q3Tp0kXy8vKyXRwAAAAASJlQKCR79uyR3r17S35+622SJJZJ0KSyX79+2S4GAAAAAKTNpk2bpG/fvq2uQ2KZBG2pdD7orl27Si6rq6uTuXPnysSJE6WwsDDbxUGWEQ+wEQ+wEQ9wIyZgIx78Zffu3aYhzcl7WkNimQSn+6smlX5ILDt16mS2g4MAiAfYiAfYiAe4EROwEQ/+FMttfwzeAwAAAABICoklAAAAACApJJYIN28zui0cxANsxANsxAPciAnYiIfgygvpGLJI+GbW4uJiqa6uzvl7LAEAAAAg0XyHFkuE5+TcsGGDWQLEA2zEA2zEA9yICdiIh+AisYShO/+WLVs4CMAgHmAjHmAjHuBGTMBGPAQXXWGTQFdYAAAAAH5FV1jEraGhQT766COzBIgH2IgH2IgHuBETsBEPwUViCUO7K6xZs4ZuCzCIB9iIB9iIB7gRE7ARD8FFYgkAAAAASAqJJQAAAJCsXbtEZsxoWgIBRGIJAAAAJKu6WmTmzKYlEEAklgAAAACApJBYAgAAAACSQmIJIz8/X/r372+WAPEAG/EAG/EAN2ICNuIhuPJCoVAo24UIwoShAAAA8LENG0QGDBApLxcpK8t2aYCM5ztcSoChk9iuWLGCyWxhEA+wEQ+wEQ9wIyZgIx6Ci8QSYR07dsx2EeAhxANsxANsxAPciAnYiIdgoius5bXXXpMBAwbIwIEDY1qfrrAAAAABVlkpsm9f0++bN4uMGiWyeLFI374inTuLlJRku4RAUgLZFfbVV1+Vs846q9ljBw4ckHPPPVfmz5/f6mu3b98ul156qbnJONak0m/q6+tlyZIlZgkQD7ARD7ARD3ALbExoUtmzZ9N9lfqjSaXSpf6tz+k6ARPYeIAUiA/Mnj1bysvLZe/eveHHtm7dKo899pgcPHiw1ddq9n3OOefIQw89JMcff7wElTZcV1RUmCVAPMBGPMBGPMAtsDGhLZWNjSLdu+tQqM2f08erqprWCVirZWDjAf5ILCdNmiRVVVXy4osvhh/r0qWL3HjjjbJmzZpWX3v99dfLJZdcElNSqUmqnahq07Cqq6szP27aAtquXbvwzcv6u169ibaj6fP6Gl1Hl/oT6X0dBQVN1afr279HU1hYKI2NjeZH19elUzbn/zjLvLw8s45ddv1dXxOJs6122WPZVv1/sZTdXsfZjmg3hTtlt7c1lrLnQj1F29ZU15OzPc77UE/erCf3tqarnhzO+tSTN+spU/uTfVyIpezUk/+Pe+5ziMDUU12dFGoilZ8veQUF4pRUj5qh+nqzNJ+Jqw78ftyz48ET9ZRj+1Mk2dyfWttmXyaWmkRqYul+rC07d+6U5557Tu688075+9//Llu2bJHvf//7Uefdue2222TmzJktHp87d6506tSpxeM6h8/w4cNl1apV5ibmIUOGyLJly8xVnEiGDRsmZWVlsnDhQhk6dKj06NHDvHe0oBw3bpx53zlz5sjkyZOlpqZG5s2bF3FdDZIpU6ZIZWWlrF69WsaPHy+bNm2SlStXNlvvlVdeMcvS0lIZOXKkrF271ryvsx0bN26M+P6DBw8Ob1+fPn3C27Fnz56I65966qnh7Tv99NPD2xGNs32LFi0Kb8fSpUsjrqt172yf1qmzHdEuMuRiPTnSXU+6X/Xu3Zt68ng9pXt/0vdU7777rpx22mnUUwbqqWDvXpn00Uey73vfk0Xvv++p/UnXVfq5B72eFMe9lS3OIYJSTx137JCJThJUUCD1egKel2d+15Px9of2k5oePTxVT7Z01pPGgxfqKVf3J1s296f9+/dL4Abv0a6wep+k+35KfUx/xo4d2+I12sI5a9as8IHwV7/6lcn+taUz1hbLfv36maCIdDNrLl0Z0f+jn8OECRPMeoG40piD9ZTJFkuNB+0N0KFDB+rJo/Xk3tZ01ZP+X/2CmThxovmyoZ4yUE8bNkjhoEESWr9e6vv08dT+pK95+eWXw98Xga4njnvhFkv7HCIw9eTsp6WlkVssKyqkbu3aFnNa+v24Z8dDUVFR9uspx/anSLK5P2m+U1JSEtPgPb5osUyUZu2anTvOPPNM+fa3vx01sdQTbP2JFBjRvlydynI4ldUae53W3jfSOm2t7wSX+3f79fZ72GXX3+2/2yp7LNsaT9ntdSKV3c1eJ5ay51I9uaWrnpz/Sz15u54SKbu9Tltld75InTJQTxmoJ+vk3Gv7k3Ni0tZ3n7NOpN99U08R1vdKPSVS9mTrKVJM+LqenP300J+f3Tjw2e/m/7VxASbesufK/qRl5Dyi0PP11FbZY9lm340Km4i+ffua1kaHk9kDAAAAAGIXuMRSu7Lef//9ZjlixAjTn/nTTz81z73//vvy5S9/WYJIk2rtS97WlQ8EA/EAG/EAG/EAN2ICNuIhuHyRWOpNtzq1iN7QqjedOhYsWCBvv/22GaBn27Zt5jFd3nrrrWapTcJPPvmk/OIXv5BnnnlGPvjgA7npppskiLTpXW/sbasJHsFAPMBGPMBGPMCNmICNeAgu3wzekw16M2txcXFMN7N6nXYB1tGiRo8eHVMfbPgb8QAb8ZAFGzY0TbBeXt5i4I9sIx7gFtiYcPbT0lK9ga35c3prlY426sF9ON0CGw8+FU++w6UEGHpVSYdS5uoSFPEAG/EAG/EAN2ICNuIhuLiMAEN3fnuEXAQb8QAb8QAb8QA3YgI24iG4uJSA8HQCOq9na/PzIDiIB9iIB9iIB7gRE7ARD8FFYokwplqBjXiAjXiAjXiAGzEBG/EQTCSWAAAAAICkkFgCAAAAAJJCYgkAAAAASAqJJQAAAODYtUtkxoymJYCYkVgCAAAAjupqkZkzm5YAYkZiCaOgoEDGjRtnlgDxABvxABvxADdiAjbiIbhILBHWsWPHbBcBHkI8wEY8wEY8wI2YgI14CCYSS4TnG5ozZw7zDsEgHmAjHmAjHuBGTMBGPARXXigUCmW7ELlq9+7dUlxcLNXV1dK1a1fJZRoGegDQbgt5eXnZLg6yjHiAjXjIgg0bRAYMECkvFykrEy8hHuD7mIh1/3PWKy3V/p/Nn9OkqqLCk/twuvkuHgJudxz5Di2WCKupqcl2EeAhxANsxANsxAPciAnYiIdgIrGEoVeW5s2bR7cFGMQDbMQDbMQD3IgJ2IiH4CKxBAAAAAAkhcQSAAAAAJAUEksAAAAAQFJILAEAAAAASSGxBAAAgD/t2iUyY0bTEkBakVgiTOcbAhzEA2zEA2zEA3ImJqqrRWbObFoiYzwbD0grah1GYWGhTJkyJdvFgEcQD7ARD7ARD3AjJmAjHoKLFksYjY2NsmPHDrMEiAfYiAfYiAe4EROwEQ/BRWIJQ3f+1atXcxCAQTzARjzARjzAjZiAjXgILrrCItwXfvz48dkuBjyCeICNeICNeIDnY6KyUmTfvqbfN29uvlSdO4uUlGSnbAHguXhAxtBiCUOvKm3YsIGrSzCIB9iIB9iIB3g6JjSp7NlTZMCApp9Ro5oe16XzmD6v68H/8YCMIrGE0dDQICtXrjRLgHiAjXiAjXiAp2NCWyo1oeneXaS0tOWPPq7POy2a8Hc8IKPoCgsAAAB/yc/XPpktH6+vb7sLLd1ngYSQWAIAACDYnC60dvdNpxutk6hu305yCbSCxBIAAADBZneh1STSpo9XVTWtQ2IJREViCQAAAETrQtta91kAYQzeAwAAAABICi2WMPLy8qS0tNQsAeIBNuIBNuIBboGPCe0q627VDPBUG4GPhwAjsUR4MtuRI0dmuxjwCOIBNuIBNuIBboGNCR0pVrvO6v2Xkehzuk7ABDYeQFdYNNG5hj766CPmHIJBPMBGPMBGPMAtsDGhA/noSLHl5ZF/AjqKbGDjAbRY4jM1NTXZLgI8hHiAjXjIAGcOPeXxefSIB/g2JuLt1qr7pEf2Sy/xTTwgLnmhUCgU30vg2L17txQXF0t1dbV07do128UBAPhpDj23NubR23Vgl8x6c5ZMGzFNuhV1S19ZAS/bsEFkwACR0tKWo7sqTRorKppaFMvKYt8HmccSAbU7jnyHrrAwtLvCihUr6LYAg3iAjXjI8Bx6ekLs/tHH9XmnRTOC6gPVMnPBTLNMJ+IBvowJu1vr4sVNj+ky4N1aAxsPSAiJJYzGxkbZuHGjWQLEA2zEQxbm0HP/uCdsP6Ryf6Vs2LXB/Gze3dRtVpfOY/p8qhEP8G1MaOKorZh9+zb9rUv9W39IKoMXD4gb91gCAJBOu3aJzJolMm2aSLfUdVHVpLHnnT2lMdT85G3UI6PCv+fn5cv2a7dLSSdOigEA6UViCQBAOlVXi8ycKTJ1akoTy321+0xS2b1jd5NAuulzVTVVZj0SSwBAupFYAgCQwzSpLMhv+XVe3+ga2RIAgDTiHksAAAAAQFJILAEAAAAASaErLIz8/HwZPHiwWQLEA2zEQwJ0TjxnapDNm5svO3fO6REmiQe4EROwEQ/BRWIJo127djJkyJBsFwMeQTzARjzEKdpE66NG+WKideIBvo+J4mKR6dObloib7+IBMeNSAoz6+npZsmSJWQLEA2zEQ5y0pVKTyu7dRUpLm//oY/qc05qZg4gH+D4mdPTmGTNSOopzkPguHhAzWixhaHeFPn360G0BBvEAG/GQIP28Clxfsz440SIe4EZMwEY8BJdvavzVV1+Vs846q9ljBw4ckHPPPVfmz5/f5usPHjwo559/vgTV7trd8sj6R8wS0C+DsrIyvhRgEA+wEQ9wIyZgIx6Cyxc1Pnv2bFmxYoXs3bs3/NjWrVvlrrvuMgljLB544AFZvny5BFXVviqZuWCmWQLafeX111+nGwsM4gE24gFuxARsxENw+aIr7KRJk6SqqkpefPHF8GNdunSRG2+8UdasWRNTYjp58mSZNWtWq+tpkmonqrt3N7Xu1dXVmR83vVKjNzA3NDSYv/V33clCoVDE99fn9TW6ji71J9L7OgoOdbHS9e3foyksLJTGxkbzo+vr0ilbXW1deKn/My8vz6xjl11/19dE4myrXfZYtlX/Vyxlt9dxtsMpm5tTdntbYyl7LtRTtG1NdT3p9uzZs8e8l74/9eTNenJva7rqSf+vxoPz/tRTG/VUVyeFIhJpi/MOLc3nEQp9VnbnNfq/ImxP3qEymM/m0OdTVx/9c7Lpeu7PNJl60tdoPNTW1kZdPyfqie+nlNWT851hx0TW6qmV/c/eD03Zo3w2fq2n1rY1lfVkx4O9fexP3qqnWI97rW2zLxNLTSI1sXQ/FouVK1dKp06d5Nhjj21z3dtuu01mzpzZ4vG5c+ea93Dr37+/DB8+XFatWiUdO3Y0I2QtW7ZMKioqIr7/sGHDTNeBhQsXytChQ6VHjx7mvaMF5bhx48z7zpkzxyTGNTU1Mm/evIjrapBMmTJFKisrZfXq1TJ+/HjZtGmT2X61o3aHWS5avEjWtF8jpaWlMnLkSFm7dq15X2c7Nm7cGPH9dVhpZ/u0X72zHXpgieTUU08Nb9/pp58e3o5onO1btGhReDuWLl0acV2te2f7tmzZEt6OaBcZcqme3NJdT7pf9e7dm3ryeD2le3/S91TvvvuunHbaadRTG/VU/8knMvHQxUg7SSxs317aHfpde9gU1deH66n97t0yKS9P8lzfZbZQfr68unSp1H7wQbPjtv6fxnaN0r59e3OioT/O70o/nx7te0SspzfeeUOe3fis3HLWLfLhux/GVE/OaI/6vrlcT3w/pb6eXnnllazXU8cdOyLufx2KisxJtSY7RSKyc+dOeWPVqkDWky2d9aTxwP40z/P1pFqrp/3790us8kLRUtccU15eLpdeemmL+yn1Mf0ZO3Zsi9doi+NDDz0kP/nJT8zfAwYMMO8TT4tlv379TFB07do1p6+MfFL5iXzu95+Tf132LxlYMpArwh6tp0y2WOoXgvYG6NChA/Xk0XrKZIulfsFMnDjRfNlQT23UU3m5FA4aJCEdBdY1eE+elrWiQkLr14uUlTUvuz33ZaR60s/+8MPDz2+o3iCD7h8kpZ1KpSC/5XXi+sZ6qdhfIWuvXCtlxWURy76uap0MvG+glP+4XPoc1ifmFsuXX35ZJkyYYMqVs/XE91NKWyz1O8OOiazV04YNUfe/QytJXkWFNK5bJw19+waqnjLZYunEQ1FREfuTR+sp1uOe5jslJSVSXV0dMd/xXYtlop544glZvHhx+N5KvWLxjW98Q3784x+bzN1NT7D1J1JgRPtydSrL4VRWa+x1WnvfSOu0tb4TXO7fCwoLwkv7Peyy6+/2322VPZZtjafs9jp22aOx14ml7LlQT9Gkq56c/0s9ebueEim7vU5bZXe+SJ0yUE/Ry25+d06sW3mNniRIXl7zsh95ZMR1nTW01HbJCwva/pyc9aJ9RonUk3Ni0tZ3n7NOpN+DvD+51/HT/hQpJjJeTzHsf+Gyx7GtfqqnSNJRT1pGziMKPV9PbZU9lm0O/w8JsCuuuML8OLTF8umnn85qmQAAAAAg1/hiVNh4aFfW+++/P+bRYgEAAAAAAUgstQvrY489Zm5o1ZtOHQsWLJC3335bnnvuOdm2bZt5TJe33npr+G80aZffrtkSwaZdK7Q7eFtdLBAMxANsxAPciAnYiIfg8kVXWB0pafr06ebHNmbMGDPSkk1HRdI5LiNpbeAev7P7dwMaBzo6GKCIB9iIB7gRE7ARD8FFFoFm86HFOi8a/E0Ha9F5YeOZuwj+RTzARjzAjZiAjXgILl+0WCIxlfsrZV9t09D22/Yd6iq8b1t4pMHO7TtLSaeSrJYR2aEjh+k8RrGMIAb/Ix5gIx7gRkzARjwEFzUe4KSy5509pTHUfP6b0x9tmghd5efly/Zrt5NcBpTOVwg4iAfYiAe4EROwEQ/BRFfYgNKWSk0qu3fsbibX1p+u7bqGf9fH9XmnRRPBopPizpkzp9WJfREcxANsxAPciAnYiIfgosUy4LRVsiC/KQzq8+o/+72RgwEAAACA2NBiCQAAAABICi2WAADkML1tIVIvE/c99JEGbtu8e3OzpWLgNgBAIkgsAQDIQZoA6u0MVTVVUdfR53W9tgZuG/XIqJgHbtt1YJf833/+T0YeGCmlhaUp2RYAQO4jsQQAIAdp4qcJoN36qAni4qmLpW/XvhFbH+2B2zSBdNPnNFHV9aIlltUHq+WZ7c/ILQdvkdIuJJYAgCYklgAA5ChN/twJoCaVZd3KYh64zcbAbQCARDF4D8KKioqyXQR4hE5qPHnyZCY3hkE8wFbQrqDZEv6h3ZxnzJ9hlvHgGAEb8RBcJJYIC4VC2S4CPKSmpibbRYCHEA+A/1UfqJaZC2aaZbw4RsBGPAQTiSXCamtrs10EeIROarxo0SImN4ZBPMAZ+GfDrg2y4dMN5m9dmr93bTDPIcfrddeGZqMEx1O3HCNgIx6CizZqhHXo0CHbRYBHFBYWypQpU7JdDHgE8ZAbiouKZfqY6WaZapFGkx37+NiYR5JV2r1y1puzZNqIadKtqJvkOj9sTypGCVaF+/bJlOXLRU47TaRbbn4WSB2+M4KLFkuENTZGnvMMwYyFHTt2EBMwiIfcoMnNjLEz0pLk2KPJlnYqlZKiErPUH31Mn3NGp01HN0sv8sP2uOvV/RNr3TZ++qnIzJlNSwQe3xnBRWKJMLrCwtHQ0CBLly41S4B4gHs02Yb6BrPUn0jTliA369X902rdVlaKbNhgfho2bjQPmaU+ps8hsPjOCC6+DQAAAPxm1y6RGTOalqmmiWPPniIDBpifwrFN3aLNUh/T59pILhMdgdZv+BzgJySWAAAAflNdbbqnmmWq7dun/R1FuncXKS2VUGmpHOja1SzNY/qcruPzrsSpwOcAP2HwHgAAAkbvm6tvbDlio3sQF+QYbSV0ErrNm5svVefOIiXRB+GJW36+TlrY9LuOAKq/tzISqA4W5NyvaY9AGy5e+86tDhIEwNtILAEACAg9cdf75qpqqqKuo8/resgxTvdU94Apo0Y1TwS3b09tcpnhEWgBeBeJJQAAAaEn7Hribrca6Yn94qmLpW/XvuYxWo1ylN09VRNIN32uqqppvSwklvYItJEGBdLn9IKHrkf8AbmJxBJh+ZG+iBBIeXl50qVLF7MEiAd/0ZN294m7JpVl3cpifg++LzzM7p5qS/Nk9XkxxoQzAq1bpK7ZyE18ZwQXiSXC2rdvn+0iwCMKCgpk/Pjx2S4GPIJ48K/iomKZPma6WcaD7wvYNH3oQEzgEL4zgotLjghjviE4dFLjDRs2MLkxDOLBv7oVdZMZY2eYZTz4voAtpC2ODQ1mCfCdEVwklgjjRAEO/TLYsmULXwowiAe4R5Otra81S/1hJFmoRs4hcAjfGcFFV1iE0bUJdjeWkSNHZrsY8AjiARFHk6377FdGkg027QrLOQQcfGcEF4klwurr683BANDW67Vr18qgQYOkXbt22S4Osox4gD2a7MZPN8roP42Whd9aKP0P72+eZyTZYAtZ5xAM1wK+M4KLLAJhJJZwaPeVNWvWyMCBA/lSAPGAZqPJ1tU3NVUe2eXINkeS1bkL7alN7CXJqPe6OEd6vPUXNoZHm204eLDppJLuj21qbb/ww77Bd0ZwkUXAt3Yd2CWz3pwl00ZMi3tgCgBA8ifPPe/s2SI50Xkzne6z2grqiRPoXbtEZs0SmTZNpFu3YHdxdonYzblz56ZpTXRezENdYYuavSi/aR3EvV94bt8A4kBiCd+qPlAtMxfMlKnDppJYAkCGaYuMnjx379jdnCjb9HFNZnQdT5w8V1eLzJwpMnVqoBJLu4uz02qmCc7iqYvN3KZRW89KSkS2bxfZ1/S6uvJyKRw7Vurmz5fCAQOakkpdB3HtF57cN4A4kFgCAIC00ZPngvzmpxuRul1mXGVlODGSzZubL1VAkiOni7NNk8q2ujmbz8b5fOoOjeTUp49IWRuvQ9T9wjP7BpAgEksAABAsmlT27NnyfsBRo5p359RWuQAklwCQCiSWAAAgWLSlUpPK7t2bEkg3fU7vH9T1SCwBICYklghj5C448vPzpX///mYJEA+wOfeFRbo/LOdoTEcaDf3QSKeIjXNsSMsxwu6yHElAuiznEr4zgovEEmGFhYXZLgI8dJFh+PDh2S4GPIJ4QKSLkFyMRNpjIlqXZRtdlj03uj7fGcHFpQSE1Tk33yPwdHLjFStWmCVAPCTImePP/vHBHH9OHBAPSHtM2F2WS0tb/ujj+nykFs1I+59P9sFMj66vy3jwnRFctFgiLC9PZ6ICmnTs2DHbRYCHEA9xcM3x1wJz/KVXQOekTIXiomKZPma6Wcb3wmKpvPJKObw4ztelo8tyW/uf837sg2nFd0YwkVgirCDSQRuBpN1YhgwZku1iwCOIhzi55vgzU1joaKOLF4v07Zvz94R5vitsQOekTAXt7jhj7Iy4X9eue3cpue8+yYn9T+X4Puh1fGcEF11hEVZbW5vtIsAj6uvrZcmSJWYJEA8J0JNWnc9Pf5yTWV3q3zl+QlvfUN9s2Rad8F3n5rN/9DEE8xgRKR5SHhOt7X8+2Ae9ju+M4KKJCmGN3HeAQ0KffipH3HOPhO69t+k+FgRaKBSSiooKswScOGgrHjq372xGjq2qidwlUZ/TdRCMY0Rb8aCICX/gOyO4SCwBtFRdLUOeeUbqbrmFxBJAQko6lcj2a7fLvtqmLombd2+WUY+MksVTF0vfrn1NAqHrIBjaigdFTAC5jcQSAACkhSYJ7kRBk4iybmWSqyr3V5rkSBMj5SwViVHw4gHAZ0gsAb9jhEQAAZtHL51JZc87eza7H1Bb3eyunNoqR3IJIIg8MXjPli1bwr//+9//zmpZAN9xRkjUJQAEYB69dNGWSk0qu3fsLqWdSpv96GP6nNPVEwCCJuuJ5cyZM+Xuu+8O/925c2e5VwcMAZBZlZUiGzY0/TgXe3TpPKbPAwBMy2RBfkGzH30MAIIs611hBw8eLJ06dQr/3adPH3n55Zflhz/8YVbLFUSFhYXZLgKyRZPGnj11aGDzpxMJhWPHNp9QWucGY5j2QM5JNmzYMO/OW4iMapffrtkS4BgBG/EQXFlPLLWF0h6OWLvCfvDBB1ktU1BxAAgwnUhak8ru3ZsSSDd9rqqqaT0Sy8DJz8+XMp37DTgUD/YS4BgBG/EQXFlPLIuLi+XRRx+Vbdu2yb/+9S954okn5NZbb812sQKptrZW2rdvn+1iIJv0RLGgQEJWPOTp40xyHGg6yfXChQtl9OjRUlCQ9a8NZFl9Q32zZbpGXVWMvJobOEbARjwEV9ZrW4Ouf//+pvvrUUcdJfPnz5fjjjsu7vd59dVXZdasWfLCCy+EHztw4IBccMEFcvXVV8tYu0ufZfny5SaR3bp1qwwcOFAeeughOeywwySI2PlhIx5gX30eOnQoLVQwnHsJ472nsLioWKaPmW6W8Yy66reRV7020q3vjxHFxSLTpzctkRGejgekVdZr/P3335du3brJ97//ffnRj35kTmZ3794d13vMnj1bVqxYIXv37g0/poniXXfdJQcPHoz6Om2Ref755+W5556TZcuWmXLcfPPNElQcAODQVsp2+flNrZUIPD029OjRg2MEkuoKq0nUjLEzWk2mWht11S8jr3ptpFvfHyN0mq0ZM5huK4M8HQ9Iq6w3SZx33nny9NNPyxe/+EXz94ABA+SGG24wSWGsJk2aJFVVVfLiiy+GH+vSpYvceOONsmbNmqiv+89//iNXXXVV+N7Cs88+Wx588MGo62uSaieqTgJcV1dnftx0h9L3bmhoMH/r79o9wL6n1KbP62t0HV3qT6T3dbco6fr2760NztPY2Gh+3LRM4a6PeZ+lE1p2/XG2I9Jr7W21yx7Ltur2xVJ2ex1nO5zP1U3Lr+vbZY2l7F6sJ2c72tpWu+wttrWuzgzGo2XMP1SXLerp0DohfZ3+fejCS4dDXWH1cbMMhaQ+xm2Np56cbQ10PXl4f9L/+89//lPOOOMM6dixY7Dqaft2adyzJ/q2du0q9d26tV5Ph/Yv/d96mpXu416696eDtU3fgzU1NVLXuS6l9eR0r3VGXXWrb/zsPaLuT9YxT3+Ptj/pu4daubimzOehx71DZa+rjx6bDi1HtBi2v1+dbU1ZPR3ablNuazvsNfOszyWV+5O+5+uvvy7jx48PDwTY1nHP+Sx1WZfievLy/hRLDDnrOXGUre8ndx3F+v1kx0NRUVFGzvc4j0jfeURr2+y5xFKTOSepVB06dJDy8vK43kOTSE0s3Y+1xX1jsbae6olTNLfddpuZHsVt7ty5zUa2dWgX3+HDh8uqVavMydiQIUNMy2hFRUXE99cRtLRM2i9duxDo1R5972hBOW7cOPO+c+bMkcmTJ5sv+Xnz5kVcV4NkypQpUllZKatXr5aBXxxoHtdEuT7vs/d3EudQXlPgrS9fL9X51eHt2LhxY9TRfZ3t05F9ne3YE+WE7NRTTw1v3+mnnx7ejmic7Vu0aFF4O5YuXRpxXa17PZht/c/W8GNr166NepHBy/Wk27Fp0yZZuXJlxPVLS0tl5MiRZvv0fSPVU8cdO2SiiPmf/evrI9bTwS1bZFJenuQd2o/0y7rI+j/m5EQPSh06RK2nvfV75aNuH8n3vvA9eX/5+zHXk26fzmXrbEdQ66lg714Z+Pzz8snZZ8vAE0/01P6k76lfYO+++66cdtppvqgn+/OuP3T7Q4t66tdP8o88UtpF+SI28vPl3eefl57HHRe1nsz/uuAC6V5fL6WHvjPSddzLxP505FFHygU9L5D3lr0na2RNSutp2dvLzGONDY1yoPZAi3UbQp+ddEXbn+xjXk2PHhG/n/T3kc53nivWCtu3F2coO+0JVVRfH66nHbU7Wnx36omzc3FW7fx0p6xatCpqPTnfv++tfE8GnjEwpnr64I03pPezz0rJLbfIsg8/jFhPznYrLYt+rtrzxGyjxnBDgzmuO59LOo57r7zySszHPf3O0Dh6a8Fb8kHBBymtJy/vT04M2fWkJ/S6jRpHToxrPfRon556srVWT05Z7bLEc76n8ZCJ8z3OI9J7Xr5//36JVV4oWuqaIddcc4386le/CmfH+uFOnTrV3PsYD01GL730UnOPpk0f059o91g6NDHVrrg6kFC0aTcitVj269fPBEXXrl1z6srIlr1bZMDdA0zXIr0qrGXSbdPEXq+K6FXhiv0V8slVn0hZt7Kca2HR9dfvXC9H33u0lP+4XPp26RvcK1gbNkjhoEFSt3at5B91VPR60ilHdNRXPXnbsEGKvvQlOfj669Lh6KOb6knvTykpiVr2DdUbZND9g2T9j9ZLn8P6cKUxXfWUpRZL/YKZOHGif1osrc9bDl1kbFFPmzdrNxoJtTJasl6Mqf/4Y1Nn2a6nTO1P+hodF2HChAlRvy8Traf1n66XY+47Jvzd5OZ8NznHdadszbbVVbcRv58++UQKjjlGQqWlZsCyFp+nlrWiQkLr15v3cMruHOcilc8p27ofrguXrcX75uWFv38/vupjGdh9YGz1tG6dtBs4UE92pL5Pn8j1dGi7pbRUQoe2qVmLZX295FVUNIv5VLZYahJhx0RKWlgSrCcv70+txZAdR2uvXCtlxamtp9a2NVI9OWW1yxJri6UTD7RY1ud8i6XmOyUlJVJdXR0x32n2GUiWffnLXzaD9WjmvWvXLtNqqF1jM0k/xLvvvlseeOCBVudy1KRLf9z0Na29zp7GI5YBUex1Yplb0l6nrfWd4IrWPcfupuOU3Sm//XssZY9lW+Mpu71OtO2w2c/HUnav1lMs22qXvcW2Hvpf5n8eejxiPR15ZMty9Otnvqzt0kYre2HBZycT8daTsw71FEM9pajs9jptld3dHcsX9WR/3q7Xu7ct79BoyS0c+tI1ZT/0/l447qV7f3JOTNr67rPL7v49WtkL2sV+WhJ1f4pSt5H2p7buIzffidYxzTnOtbUdsdSrs60pqyfrf9rb5d7GSDGfquNepJho9bgXQcR6OnSbRguHTqrd9eTl/SmWGHLWc29Ppr+fnLJGKkss30/6Guf/5vpxL5GyZ/w8IoJUnEfEM8+9JxLLBQsWmKZaLbh2s2orG04lzfTvvfdeue6662LqPgt/j2wHAAA8onPnpos2rtudmtHndb0A4fwLXuWJ4Zp69eol55xzjpx55pkmqdREM54bReOh3T3vv//+cJfW22+/Xb71rW+Fk8pnnnkmfJ8EgjeyHQAA8IiSEpHt200XYPOzeHHT47p0HtPndb0A4fwLXpWVFku93/HJJ580CeXXvvY10/3VoV1stEvsp59+GvP76U23jz32mLmhVW861fuAlCaob7/9tkka9QZW/X/btm0z81aeddZZ5n5MTTKfffbZ8P/W99K5LwG0jYnMAeS0NrpYRn1ZqLHZCLXOY0gDTRrdiWPfvuH7RAEEPLHUZK53796mT/yMGTPM4Dd6g6/jqaeeCk9xEQsdKWn69OnmxzZmzBgzGJBNR0XSOS7VJZdcYn7QJNL9o56xa5fIrFki06YxF1UGOP3wW+uPH4SJzCHhONDR7mK5PwP+54t4SLCLpV4s0+NaVU3k1+lzuk7Q+CIm/C6D51HEQ3BlpcZ1sB47ifzxj3/c7Plzzz035qQSqeMeuMdTqqtFdKqXqVNJLD3CnshcT6bc9Dk9+dL1SCxzn44GC/gmHpwulodGwhYdAXjUqKYultoapjSpdLWU6bFML5bpcU17Z+iFtMVTF0vfrk2vCXIvjZyPCb/L8HkU8RBMWb+UoPOiuYfK1UlVdW6XtkZGQmodOHDADAsNmGG+rWVrYpnIHLlN48CZlyue0eHgT56MB2u6JJMk2ssoSWKiXSw1cbSTR00qdVouz9w2kGD3Xt/FhM8kFEOJ7BcpQDwEV9YTS52z8uc//7mMHj063GT+t7/9Tc4+++xsFy1wSCoRT1fYVGF0O+9/dhoHeoJAtyZ4Mh705Llnz5aJk7ZAOvRCdZYGecnYbQNZHEE1YzGh8ynrbU+6DJCEYiiL+8Xe+r3y9mFvy+j60XJ44eEpfW94W9a/FXSgnvLycjn66KObDd6DzNPP3tPdYeHr0e2mDptKYunhz66mpoYpmeDNeNAWGT157t49PJ9oM/qcJlu6XhYSy4zdNpBg996cigntwjljhgRNQjGUxf1Cv5tuWXyLfOeL35HDO5JYBknWE8vLL79cqqurzWisxcXF8o1vfEPOO++8bBcrkHQKFk+1WrbWhSONX46Iryss/E/jYN68eXRrgrfjQU+eI7WYReoWmgsy2L3XtzHhMwndepLAfuF0u02023Z9Q32zJYIj64nl1Vdfbab9+OY3v2kSy5tvvtnMKzl06NBsFw3Z1FYXjix2awIAIFZtjSQbcTRZj3fvRbC63TLaO3ImsVy8eLEsX748PHrUFVdcIffcc49MmjQp20VDNrXWhaOV7ht6z5kzYXCkK23FRcUtugzaN8RHEuRR/rIxCAGfd+uYOzQJWRrIAsFmjySrYhpN1uPde5G8SHOhOo97tdsto73D84nl2LFjWwxJPGDAgGZ/79ixQ7p3784UJEEUqQtHK903dCATvefMZl9pmz5muswYO6PNG+KbFYGrcxkdhIDPW/wxd6jX5p6lBQgpphcq9TtFl21xjyQb62iyvuvei8RasD3S7ZbR3uH5xPK0006T7du3S0/9wheR2tpaM5iP7dlnn5Urr7wySyVELtHRMXUgk2hXhd0nAMzFmHlcDU1cTsWr1+aepQUIKaa9X+wLlUDaWrCBHJH1xPL222+X6667rtmclTo66Sy90m2NEktiiVi/6N1dXWO5KsxcjJnH1dDEEa9JSKQFKAvzAgLwr0RbsDPZfTbS/2rt/9i3aWzZvSW8LCxoGsyJZDkYsp5Y/uEPf5Dhw4e3us7SpUtNS2aHDh0yVq4gYqoRANF4Zs7CTMrivIBeF8h4QKuICX90n23rf0X6P9Fu0xj7+Fjv3aaBtMr6UaCtpFKdeuqpGSlL0JG4w+EMF8+w8XDiYMqUKRI4WZ4X0KsCGw+IipjwT/dZ9/9yi/R/cuo2Dfg7sYR3NDY2NuuSjOCOfmliwVoi2DQOKisrpaSkJHjHiCzNC+hlgY4HRERMpF/CA0Al0JU/0v+K9zYN+5yS2zSCg70fzSY4zshIkTNmNC2R3tEvdXRl/XFGvdSl85g+r+tFoV8I9hLBpnGwevVq4gEG8ZAjiotFpk9vWqYZMeHBEYntrvwVFS1/9PE0duXPyDklPIcWS4S1b98+eCNF+lEKRr907pXhnhk4cTB+/PhsFwMeQTzkCP2O1Qu5GUBMeHBE4ix35c/IOSU8h7NGhDU0NDBXqJ8kMf8ZXWE90GXZQ/fuaRxs2rRJ+vXrRzc3ZCQeEhr90o+j9+bINnGM8KgsduXnnDKYSCwRVldXx0EARsNhh8m/L7hABh52GIllurssu08Qna7LenKmV5s9kFzqCcLKlSuld+/e6T9p1G7yOt3UtGn0avCodMZDQqNf+nH03hzbpoweI5ATOKcMJhJLAC116yZrLrxQBnJin50uyzF0V/YtussHWkKjX7q7/EXioR4AMWFEYgA5iMQSALzWZZlBDxBgCY1+GanLX65jRGIAOYb+CkCSdh3YJTPmzzBLAECGR7/MsRFUARsxDj8hsQSSVH2gWmYumGmWAIDUj36py4yMoEr3a2QYMQ4/oSssgMyP3piAyv2V4fuuImlx3xUAAAAyhsQSYYzkBkdeXp6UlpaaZUpHb0wiqex5Z89Wk1X9XzroB8ll67TL9qw3Z8m0EdNivkKeV10tX/zHPyTvtNP8dx8b0nJ8gAe6PmYQMQE3zimDicQSYUxmC3uy65EjR6Z+9MYE6f/QpLJ7x+4mgXTT5zTB1fVILGPruj112NSYE8uCffuk38MPi/z85ySWiOn4gCQnt88xxATcOKcMJhJLhNXX15svh1i7I2oiYS9bTSScieDdk8CbFzFkutfonGRr166VQYMGtToPVUKjNyZBk8qC/JYxGqkrLlI80bW1RLDFenxAcBATSOScEv5DjSMsFAol1B1RW6la7Y4YaSJ4ZxJ4j00Ej8/U1NRkuwgAPIrjQ4blwIiexATiOadMxW0a8B4SS4QVFhampzsiE8HnHL3iPHz48GwXAx7htEDQEgHF8SELnBE9PYqYQLznlKm4TQPeQ2IZcPaInna3hbYGSUmoOyITwedUt6ZVq1bJ8ccfTzKB+LrC0u3d9zg+wI2YgFtdXV3cyWVMnO+YaPieySoSy4CKOqJnbepH9ETuaWxslI0bN8rQoUM5SYCJh3bWMqpsdXvftUtk1iyRadPSO0dbDnRHzASOD3AjJhDpYkPKE8tI3zFubXzP0OU2vUgsA8o9omf5znIZ+/hYmf8/82XAEQPMY8wLCCAu2er2Xl0tMnOmyNSp6U0sPd4dEQAC+x0T4/cMXW7Ti8QywOwRPevq68yyT9c+aRvRMyF6kHB3l23tShVyshu2/Rh8IN5u73bXpli70CbymmjHFOdxAEBKvtcbQg3h31P+3R7pO6aV75mEZzRA3Egs4U16UqgHDr3yFIk+p+sg5yb9jtoN+xC6YAdMtK5NrXWhTeQ1bR1TnNdwXAGAuET9Xt+f/e/2hGc0QEJILOFNejKoJ4V2i4SeNC5eLNK3Lzdn5/Ck3+5u2HrVUA/wi6cuNnNgcuUwYBLp2pTIa9o6piiOKwDgq9urEp7RAAkhsYR36Qme+yRPTwDLPNRVF0l3w3ZoUumpbtjIrDi7NiWEYwoAeOb2qkx2UU14RgPEhcQShnMVJ9LVHARPfn6+DB482CwBJw48Ew90a80qjg9wIyYQ7zllznVRzdTo4zmOxBIGE6DDpnEwZMiQbBcDHuG54wPdWrOK4wPciAnE+52Rc11UMzX6eI4jsYRR31DfbBk0kUYodR4P4ghj9fX1smzZMjnllFOkIFL3RASKxkOBtUyLeEdrpVtr1nB8gBsxgUTPKemi6i/s/fBtV9hYRilta4TSSCOZZbX7Roa6Ymh3pj59+mSkW1Oso8lmBV1f0t8VNlvdWouLRaZPb1rCs8cH5AZiAn4/p0zErgO7ZNabs2TaiGmBmTOTxBLevIcqQ6OUtjVCaaTWx6x238hQVwyNg7IMtfzEOppsVtD1Jf3Hh2x1a9X6nOHRuPO4TB4fkBuICfjynLK1+ZKjfC9Vunq0zVwwUyYcPSHqOaXfkFjC6FzQWb7V/1tmGTSJjlDq5+4b2q1p4cKFMnr0aLo1xcuHrZxp7wpLt9acwvEBbsQEfHdO2dZ8ye65krPdo80j2PthFHcolq8d8TWzBEKhkOzZs8csEScftnI6cUA8QHF8gBsxAd+dU7Y2X3KkuZKz3aPNI0gskfaBblodnCPawByIrStGa90E4x0MBdnh1f3CjrstWz5bFha23T3Vq9sEAMj9c8oEzm8SPneNNMdyG/Mr5/u4R1tbSCwRs0QGuolpcA7mm0u8K0ak7hjM8ZcbvLxfuOLuUCophWPHNi+fqxuQp7cJAJDb55QJnN8kfO6KhJBYImaJDHTTYnAO5ptLXVeMaN0xmOMvN7jryS2bdeSKO+3cdvDgQenQoYPktdINiH0dAJC2c8oEzm8SPndFQkgskZGBbloMzsHAHPGJ1BWjte4YDIaSGyLVUwwS7tKTTNxprNm/R8O+DgBI1zllAuc3CZ+7Km7tiAuJJXIDc84BwejSw74OAMj23Nnc2pEQEksY7dq1k1NPPdUsPYk55zLK8/EQUNns0lPYvr1kBPu653F8gBsxgVyJh5jnzm6r2y23dkREYonwJLY9evTIdjHgEcSDdyXSpWfXgV1SfaA6nIzaS6VXbvXLNloXIL2vsp3d/YduQIHG8QFuxAR8GQ8J3lbUmKnbVTwowmgguenVV1+Vs846q9ljBw4ckHPPPVfmz58f9XWbNm2S3//+9/L000/Lo48+KkFVV1cnL774olmmvDtBDvHjNiXStTCReIB3zXpzlgy4e4D5cSZq1qXzmD4ftQtQRUXLH328rW5AdGn1LY4PcCMmkJV4yOT3TAz/q7N1u0rF/ooWP/p4zt+uEoQWy9mzZ0t5ebns3bs3/NjWrVvlscceMyMZtubmm2+W3/3ud5KXlyeLFi2Sp556Si666CIJmoKCAjn99NPNMuXdCXKIH7cpka6FicQDvGvaiGkyddjUqN1nW1xIcXUBCm3aJHmnny6hRYskr1+/pnXa6gZEl1bf4vgAN2ICWYmHTH7PxPC/ShiB1h+J5aRJk6SqqspcHXF06dJFbrzxRlmzZk3U173//vtSW1trkko1YsQIuf7666Mmlpqk2onq7t27zVKvyES6KqNdAbR/eUNDg/lbf6+vr5dQSAfvb0mf19foOrrUn9au9jg7rK5v/x5NYWGhNDY2mh9dX5dO2bRM+pj+P/089Ef/tsuuv+trInG21S57xG2tqzNz4un7aHO5/r9Yym6v42yHUzY3p+z2tsZS9ljrKdr7uNXVN8VFUvV06LHIJTm0vSJNZW9oSFk9OfGg76PrZ7ue9LOM5zNP2f50KF7N61yvbbE/7dghDXpM2LKl6TXl5eHX5B12mBT06pXy/cn5XJz3iVZPndt1ls6HWhed1/Tp0kd6d+4d3o4Wn41elS0uNvXU7tD/re/VSwrLyj4ru1eOe/HUUwwxmfLjnmtbs70/JVtP+r86duwY/r5I9/dTtG2lnrxzHuE+h7C3lXry1vletG1NZT3Z8aDLWOrJ+W7S1+pPrtZT947dpbiw2PzuvG/PTj3N9214Ww+9R7brKdK2RqqneFqefZFYahKpiaX7sbYsX75cevXqFf5bK3jjxo3NdlzbbbfdJjNnzmzx+Ny5c6VTp04tHu/fv78MHz5cVq1aZb6EhwwZIsuWLZMK7UoWwbBhw6SsrEwWLlwoQ4cONf3T9b2jHTzGjRtn3nfOnDkyefJkqampkXnz5kVcV7dnypQpUllZKatXr5bx48ebbsArV66MuH5paamMHDlS1q5da97X2Q79fCIZPHhwePv69OkT3o49e/Y0W6/jjh0yUUR27twpJUcdZbZPr2o52xGNs33aquxsx9KlSyOuq3XvbN+WLVvC2xHtIkO89VRyTEl4R8svbNpZ9YKDs7M2hJp2eq2LHu17JFdPAweax80FjQhxkB8KiQ6psn79etlTXZ2yenKcfPLJ0rt376zX047aHeHfnRMX/az0wpA5ELs+81TtT2/Om2fiVd+3xrpfpEU9HX+85B15pBRaB/jCsWPDv4f0RGvHDllbWZnS/cn5XHZ+ulOOOiK2/cl5zd59e2Xukrkx1dPp/fubxO29996Tk445JqX7UyrqadFbb8nACy6QT956S+o/+MBzxz2HDmbhbF+uHffsetJ19ftTP18/fT/5rZ78eh7hoJ78VU976/fKBT0vkM4Fnc17+aGe8uvyzTa9teAt+aDgs++mXKun/fv3S6zyQtFS1xyjXWEvvfTSFvdT6mP6M9Y6yXPcfvvtpnLsZFGDTivBTjhba7Hs16+fCYquXbvm9BUs/T+vvPKKTJgwwayXtisjGzZI4aBB0rhuneQfdVROXmncvGezHH3v0VLaqVQK8ltegNAbtrUv/dor10pZcVly9bRli8iAARIqLY06j2VeRYU0fPKJuZk8ZS1hh+JBewN06NAh6/W0oXqDDLp/UMyfecr2p08+MfFat3Zti5v1I9ZT9+5N9x7aGhslTy98lZdLw6EJnFNVT87nsu6H60xiGUs9Oa9Z/6P10uewPjHVU6i83HwOBz76SIoGD876FeFcunIfaVuzvT8lW0/6mpdffjn8fdFW2aknb7ewpKKe3OcQ9rZST8E77tnxUFRURD15tJ5i3Z803ykpKZHq6uqI+Y7vWiwTpZm5+6qGVpJ+eJHoCbb+RAqMaF+uyh5uOZb+5vY6rb1vpHXaWt8JLvfv9uvt97DLrr+3NXS0XfaI23rovZ3/G0/Z7XUild3NXieWssdaT23933BZC5p/lonWk4rc2cxV9kPlT0k9WeWIt+zpqCf9LGMZac39madqfzLLCK9tUU/6u/vztL5UUr0/OZ9LPPXkvEa/pGKtJ+dr1ClvKvcnLxz33FJ+3Eui7F457tnrOCcmbX332WV3/95W2amn3NyfIsUE9RTc456W0SvnETbqKYFzoKCNCpuIE088sVkTsmbp2gIZywcPIHNSMdKaTrkxY/4Ms/QLX45iDAAAclLgMijtyvqHP/xBvvvd78oJJ5xgrtrrfVrt27eXJUuWyFVXXZXtIgJIw0hrOo/jzAUzzeioEedszEG+HMUYAADkJF8klnrTrU4toje06k2nEyfqcBsiCxYskLffftvc3Ks3sOp9k9u2bZNbb73VzHmpN7Lecccdct9990n37t3NvJeXX355tjcHQASaNLoTR00qy7pFn6i4cn9ls2TUXkZNRisrw9NsyObNzZdtTbOh9zq476eIcRRhAACAXOaLxFJHSpo+fbr5sY0ZM8aMtGTTZFLnuHRo19drrrkmY2UFkJkuoJpU9ryzZ/j+S4e2dDq0+6y2hIaTS00qe/ZsmQyO+uw1ZnAenePRTi412dTHXaNTN3vNoek+AAAA/MgXiSWSp/eV6tDI3F+KXIiHWLqAakulJpU6p5QmkG76nN6bqeuFE0ttqdSkMtLoruZFjU3Jo65nJ5b6uyab+ri2bmoiunixyKFRYFtt5cwBThx4NR6QWV4/PiDziAnYiIfgosYRplOvxDL/J4LBL/GgSWW0KUqivyjC6K7mRa28RhNHO3nUpNI1RQngF345PiB1iAnYiIdgCvSosPiMzlWjk9G2Nt8OgoN4gK2+c2f594UXmiXA8QFuxARsxENw0WKJ8Bw1U6ZMyXYx4BHEA2yFpaVy7FNPZbsY8AiOD3AjJmAjHoKLFksYjY2NsmPHDrNEbPQePe1O6f5xDxaTi4gH2IgH2IgHuBETsBEPwUViCaOhoUGWLl1qlmidTlGh9+3pwC8V+yta/Ojj+ryul6uIB/+KZURdN+IBNuIBbsQEbMRDcNEVFoiTjiCqU1TY8yPqFBaLpy428ypGnR8RyJERdQEAAOJFYgkkQJNGd+KoSWVZN0YBBQAAQPDQFRYAAAAAkBQSSwAAAABAUkgsgSwMhgIAAAD4CfdYwsjLy5MuXbqYJeLjx8FQiAfYiAfYiAe4EROwEQ/BRWIJo6CgQMaPH5/tYsAjiAfYiAfYiAe4EROwEQ/BRVdYGDqJ7YYNG5jMFgbxABvxABvxADdiAjbiIbhILGHozr9lyxYOAjCIB9iIB9iIB7gRE7ARD8FFV1iEuy2MHDky28WARxAPsBEPsBEPcCMmYCMegosWSxgNDQ3y0UcfmSXSb9eBXTJj/gyz9CLiATbiATbiAW7EBGzEQ3CRWMLQ7gpr1qyh20KGVB+olpkLZpqlFxEPsBEPsBEPcCMmYCMegouusEAGVe6vlH21+2Tz7s3mb2epOrfvLCWdSrJYOgAAACAxJJZABpPKnnf2lMbQZ1fwRj0yKvx7fl6+bL92O8klAAAAcg6JJZAh2lKpSWX3jt1NEmnTx6tqqsw6JJYAAADINSSWQIZpUlmQ33zXq2+sz1p5AAAAgGQxeA8AAAAAICkkljDy8/Olf//+ZgkQD7ARD7ARD3AjJmAjHoKLrrAw2rVrJ8OHD892MeARxANsxANsxAPciAnYiIfg4lICDJ3EdsWKFUxmC4N4gI14gI14gBsxARvxEFwklgjr2LFjtosADyEeYCMeYCMe4EZMwEY8BBNdYRHutjBkyJBsFwMeQTzARjzARjzAjZiAjXgILlosYdTX18uSJUvMMq2Ki0WmT29awrMyFg9+5MMYJx5gIx7gRkzARjwEFy2WMEKhkFRUVJhlWnXrJjJjRnr/B3InHvzIhzFOPMBGPMCNmICNeAguWiwBAAAAAEkhsQQAAAAAJIXEEgAAAACQFO6xBDKsMdQo9Y31LR5r/UWNejd85McBAACALCOxBDKkc/vOkp+XL1U1VRGf1+d0neYv6iySny9SFfk1TS/Mb1oPAAAAyBISS4TnHBo2bJhZIj1KOpXI9mu3y77afbJ592YZ9cgoWTx1sfTt2tc8r0mlrtP8RSUi27eL7NvX9PfmzSKjRoksXizSt+l1JqnU9VKIeICNeICNeIAbMQEb8RBcJJYw8vPzpaysLNvF8D1NHO3kUZPKsm5tfO6aNLoTR00q01hfxANsxANsxAPciAnYiIfgYvAeGDqJ7euvv85ktl5XXCwyfXrTMo2IB9iIB9iIB7gRE7ARD8FFiyXCV5eGDh1qlvCwbt1EZsxI+78hHmAjHmAjHuBGTMBGPAQXiSUM3fl79OiR7WLAI4gH2IgH2IgHuBETsBEPwcWlBBh1dXXy4osvmiVAPMBGPMBGPMCNmICNeAguWiwRRl942AIdD8wb2kKg4wEtEA9wIyZgIx6CicQSABzMGwoAAJAQEksAiDZvaCRpmDcUAAAg15FYAkBb84YCAACgVQzeAwAAAABICoklAAAAACApJJYwCgoKZNy4cWYJEA+wEQ+wEQ9wIyZgIx6Cyzc1/uqrr8qsWbPkhRdeCD/27LPPSkNDgxw4cEBOOeUUOe6441q8bvXq1fLaa69JaWmpvPfee/Kzn/1MunbtKkHUsWPHbBcBHkI8wEY8wEY8wI2YgI14CCZftFjOnj1bVqxYIXv37g0/tmDBAqmtrZULL7xQpk6dKvfff780RpiDbsaMGfKjH/1ILrroIpk0aZL85je/kaDONzRnzhzmHYJBPMBGPMBGPMCNmICNeAguX7RYakJYVVUlL774Yvixe+65p1mSqC2SL730kkyZMqXZazdt2iR5eXnm9yOOOKLVneDgwYPmx7F7926zrKurMz9u+fn50q5dO9NqqvR3ff9QKBTx/fV5fY2uo0v9ifS+DqeLga5v/x5NYWGhSa71R9fXpVM2LdOECRPMUv+nfia6jl12/T1Scm5vq132WLZV/1csZbfXcbbDKZubU3Z7W2Mpeybrqa6+Lrx0v7a1eoq2ramuJyce9HlTzhysp2jv4+bUQSr3p2jbmsv7kx5nHV7bn6inzB/3Jk+eHP6+aKvs1FNufT8lUk/ucwh7W6mn4B337HjQ96GevFlPse5PrW2zLxPLLl26mMTStnz5cunVq1f4b/1dH3MnlhMnTpRp06aZlssPPvhAfvKTn0T9P7fddpvMnDmzxeNz586VTp06tXi8f//+Mnz4cFm1apXpEjBkyBBZtmyZVFRURHz/YcOGSVlZmSxcuFCGDh0qPXr0MO8dLSi1/7q+r14V0i/5mpoamTdvXsR1NUh02ysrK0333/Hjx5ukeuXKlRHX10R85MiRsnbtWvO+znZs3Lgx4vqDBw8Ob1+fPn3C27Fnz56I65966qnh7Tv99NPD2xGNs32LFi0Kb8fSpUujxoOzfVu2bAlvx5o1ayKun8160mWP9j08W08jRoyQnj175mQ9lRxTEj4g5hc2HVT1wpBzUG0INTSrA/an6PWkn8369etl586dnt6fgl5PmTru6W0l+hotF/Xk3Xpif2qOeqKeqKeFCdXT/v37JVZ5oWipa44pLy+XSy+9VObPn2/+LioqMh+80xr58MMPmw/3wQcfbPY6PdH89a9/LdXV1aZC77rrLjnyyCNjbrHs16+fCYpI92Xm0pUR/T+vvPKKucKk63GlMb319EnVJzLo/kGy9sq1UlZc5rkrWE48aCtVhw4dcrKeNu/ZLEffe7SUdiqVgvyW19DqG+ulYn9FuA78eqUxFfWk/1e/YPRCnH7ZeG1/op4ye9zT17z88svh74u2yk495db3UyL15D6HsLeVegrecc+OBz0fp568WU+x7k+a75SUlJhcqa1xaHzRYhmJZt06aI9z87AmhNry4nbxxRfLo48+Kp07dzZXRM455xyTgDoJqU1PsPUnUmBE+3J1KssRywhZ9jqtvW+kddpa3wku9+/26+33sMuuv9t/t1X2WLY1nrLb60Qqu5u9Tixlz2g9FRSGl5Fe21Y9tVb2VNaT839zsZ7a+r/hsrrqIJX7U2tlz6X9yfkidcrguf2Jesrocc85MWnru88uu/v3tspOPeXm/hQpJqin4B73tIy5fB4RlHpqq+yxbLOvBu+J5MQTT2zWPLxt2zY5+eSTm62jTd+bN282SaXTDKyZv7tbLQAAAAAggIml3jep/YTtxFL7O2vLpY4Qq0sdrEf7GuvosUofO+yww0xzLwAAAAAgNr7oCqstj4899pi5oVWTSb0PaMyYMea+y8cff1x27dolV111lWle1gTz1ltvlbPOOsvcyHrffffJTTfdZG7K1fXd92ACAAAAAAKQWOpISdOnTzc/tksuuaTFuppMbt26Nfz32LFjzQ8AAAAAIDG+7QqL+MVyUy+Cg3iAjXiAjXiAGzEBG/EQTNQ6wiM+uef4RHARD7ARD7ARD3AjJmAjHoKLFksYOhrujh07os6Hg2AhHmAjHmAjHuBGTMBGPAQXiSUM3flXr17NQQAG8QAb8QAb8QA3YgI24iG46AqLcF/48ePHZ7sY8AjiATbiATbiAW7EBGzEQ3DRYglDrypt2LCBq0swiAfYiAfYiAe4EROwEQ/BRWIJo6GhQVauXGmWAPEAG/EAG/EAN2ICNuIhuOgKC8DXGkONUt9YH/FxAAAApAaJJQBf6ty+s+Tn5UtVTVXUdfR5XQ8AAADJIbEE4EslnUpk+7XbZV/tPvP35t2bZdQjo2Tx1MXSt2tf85gmlboeAAAAkkNiCcC3NGl0J46aVJZ1K8tamQAAAPyIwXuALCguKpbpY6abJTKDzxwAACB9aLGEkZeXJ6WlpWaJ9OtW1E1mjJ0hXuXHePD6Z+5lfowHJI54gBsxARvxEFx5oVAolO1C5Krdu3dLcXGxVFdXS9euXbNdHAAAAADISr5DV1gYOtfQRx99xJxDMIgH2IgH2IgHuBETsBEPwUViibCamppsFwEeQjzARjzARjzAjZiAjXgIJrrCJoGusAAAAAD8iq6wiJt2V1ixYgXdFmAQD7ARD7ARD3AjJmAjHoKLxBJGY2OjbNy40SwB4gE24gE24gFuxARsxENwkVgCAAAAAJJCYgkAAAAASAqJJQAAAAAgKSSWAAAAAICkFCT38mBzRrvavHlzzk83Ul9fL5WVlbJlyxYpKCAsgo54gI14gI14gBsxARvx4L/pRlQso/xS20n4+OOPzfK4447LdlEAAAAAIG15z8knn9zqOnmhUCiUnn/vf59++qkcccQRsmnTppxvsQQAAAAAd4tlv379ZOfOnXL44YdLa2ixTEK7du3MUpPKXE8stXl71apVcvzxx4e3C8FFPMBGPMBGPMCNmICNePCnWOqSwXtgMJktbMQDbMQDbMQD3IgJ2IiH4CKxBAAAAAAkhcQSAAAAAJAUEksAAAAAQFJILAEAAAAASSGxBAAAAAAkhcQSRn5+vgwePNgsAeIBNuIBNuIBbsQEbMRDcOWFQqFQtguRyxOGFhcXS3V1dc7PYwkAAID0GHPnS7J998G4XtOzawdZcO2ZaSsTkOp8pyCmd4Tv1dfXy7Jly+SUU06RggLCIuiIB9iIB9iIB7gRE23TpHJNu6/G96Ldf5VcRDwEF23UMLS7Qp8+fei2AIN4gI14gI14gBsxARvxEFxcRoChO39ZWVm2iwGPIB5gIx5gIx7gRkzARjwEF5cSEO628Prrr5slQDzARjzARjzAjZiAjXgILhJLGDqG0549e8wSIB5gIx5gIx7gRkzARjwEF4klAAAAACApJJYAAAAAgKSQWAIAAAAAkkJiCQAAAABICoklAAAAACApJJYw2rVrJ6eeeqpZAsQDbMQDbMQD3IgJ2IiH4CrIdgHgnclse/Toke1iwCOIB9iIB9iIB7gRE7ARD8FFiyWMuro6efHFF80SIB5gIx5gIx7gRkzARjwEl69bLA8ePCgXX3yxzJ492/z97LPPSkNDgxw4cEBOOeUUOe6448zjmzZtkpdeekm6du1qnrv00kslaAoKCuT00083S4B4gI14gI14gBsxARvxEFy+brF84IEHZPny5eb3BQsWSG1trVx44YUydepUuf/++6WxsdE8d/PNN8v3vvc9+cY3viEDBw6Up556SoKoY8eO2S4CPIR4gI14gI14gBsxARvxEEy+vZSgrZSTJ0+WWbNmmb/vuece+c1vfhN+vrS01LRS9u/f3ySceXl55vERI0bI9ddfLxdddFHEFlD9cezevdsstak/UnO/9jHXG5e1lVTp7/X19RIKhSKWWZ/X1+g6utSf1roROFeCdH3792gKCwtNMq0/ur4unbLp/3nllVdkwoQJZj39PHQdu+z6u5OMR9tWu+yxbKv+31jKbq/jbIdTNjen7Pa2xlL2XKinaNua6npy4mHSpEnSoUMH6smj9eTe1nTVk/7fOXPmyMSJE83JAvXkzXrK1P6kr3n55ZfD3xdtlZ168v9xz30OYW8r9eSIvB2tC7XYvlzYn+x4KCoqyrF6+gzfT/VmGU+XZl8mlitXrpROnTrJscceG35MWy579eoV/lt/18e2b9/e7HGt5I0bNzYLCsdtt90mM2fObPH/5s6da/6fmyatw4cPl1WrVpmTsSFDhsiyZcukoqIiYrmHDRsmZWVlsnDhQhk6dKi58VnfO1pQjhs3zryvnvBpEl1TUyPz5s2LuK5uy5QpU6SyslJWr14t48ePN12A9bOy6YHASbxHjhwpa9euNe/rbId+NpEMHjw4vH19+vQJb8eePXsirq+jhTnbp90lnO2Ixtm+RYsWhbdj6dKlEdft0qVLePu2bNkS3o41a9ZEXD8X68mR7nqqqqqS3r17U08er6d070/6nurdd9+V0047jXryaD1lan/SdZV+7tSTd+spG/uTcw6hqKfm9dRQ3yAS5yCp+hrdplzdnzQecq2evLQ/2bK5P+3fv19ilReKlrrmKG1FfOihh+QnP/mJ+XvAgAFSXl5urphoRTgtkw8//LD5sPV5fdxOGDXw9Dk74YzWYtmvXz8TFHp/Zi5fGaHFMjfqKdq20mIZzHpybystlsGuJ1osqSdaLL1bT1+4+QVZ0+5rEo/BDX+R9286K+f2J1os63OiniJta6R60nynpKREqqurI+Y7zT4D8ZknnnhCFi9eHL63Uq9C6L2TzqA9Tp9vTRB79uxpsnP3lQ1dVz9ANz3B1p9IgRHty1XZ8/jEciOzvU5r7xtpnbbWd4LL/bv9evs97LLr723NSWSXPZZtjafs9jqRyu5mrxNL2XOpntzSVU/O/6WevF1PiZTdXqetsjtfpE4ZqKdgH/ecE5O2vvvssrt/b6vs1FNu7k+RYoJ6cjQ1bMQnL+r25cL+pGXkPKLQ8/XUVtlj2ebw/xCfueKKK8yPQ1skn376afnqV79qmou1aVht27ZNTj75ZNPiaHfd0BMofSyWDx8AAAAA4PNRYW3Tpk0z/YYdmlhq/+cTTjjBNC/rAD5qyZIlctVVV2WxpAAAAACQWwLTLDdmzBhzr+Xjjz8uu3btMsmj09x8xx13yH333Sfdu3c33WUvv/zybBcXAAAAAHKG7wbvySS9mbW4uDimm1m9TsPAudnYGeAIwUU8wEY8wEY8wI2YaNuQn/9N1rT7alyvGdzwV/noF1+RXEM8BDffCUxXWLRNR8cFHMQDbMQDbMQD3IgJ2IiHYCKxhKFXlnTOoNaGRUZwEA+wEQ+wEQ9wIyZgIx6Ci66wSfBTV1gAAACkR5C6wsJf6AqLuOkEqzt27Ig60SqChXiAjXiAjXiAGzEBG/EQXCSWMBoaGmTp0qVmCRAPsBEPsBEPcCMmYCMegiuhxHLZsmVy9913h/9+7bXX5OOPP05luQAAAAAAfk4sNZE8/PDDw3+fccYZcvvtt6eyXAAAAACAHFGQyIuGDh1q5qixvfnmm6kqEwAAAADA7y2We/fuldraWvO7jhB01VVXSa9evVJdNgAAAACAXxPLKVOmmPlphg0bJgMHDpSqqip58sknU186AAAAAIA/u8LqHCb24D0qliGFa2pqpEOHDpKfnx++V3PlypXyuc99TiZPnpxIUZAieXl50qVLF7MEiAfYiAfYiAe4EROwEQ/BlRdy3ywZo3/9619SWVkZvtdSWzCvvfZakzhGc8IJJ8iCBQukW7ductttt8krr7xiEsqFCxfKscceK3feeaf4dcJQAAAABNOQn/9N1rT7alyvGdzwV/noF19JW5mAVOc7CbVYTp061SSWgwcPDrc+vvvuu/Kzn/2s1dfV19ebpFI999xzsmTJEmnfvr1cc801plstskdbnDdt2iT9+vUL1ymCi3iAjXiAjXiAGzEBG/EQXAklluXl5S1GgX3vvfdMILUWQD179jTJ5MiRI6V3796mxVOXe/bsSaQYSCGtuy1btkifPn04CIB4QDPEA2zEA9yICdiIh+BKKLHULq1uJSUlbSaWDz/8sFx66aWmlbJz587mfU466STZsWOH/PrXv06kKEiRgoICk/ADiniAjXiAjXiAGzEBG/EQXAkllt/61rfkhhtukM9//vPhx/R+yUceeaTV1w0YMEDmz58vH374oaxdu1YuvvhiczXj5JNPlnbt2iVSFKRIQ0ODqZNBgwZRFyAe0AzxABvxADdiAjbiIbgSSiy/+93vytFHHy1FRUXhEZ/WrVsXc3O3JqR2Uors09bmNWvWmOljOAiAeICNeICNeIAbMQEb8RBcCSWW2sr45z//udljX//6180Vinj6Uus9lto9dunSpbJt2zbzWK9evUzzuXaZLS0tTaR4AAAAAIAMSuiO2jFjxkhtbW2zxzSpjGfmkuXLl5spRu655x4zhO3o0aPNj/6ujw0ZMkTefvvtRIoHAAAAAPB6i6V2e9Uk0OnOqgnlypUrzZQjsfrhD38o559/vjz44IMtJlDV9/v+979v1tHWTAAAAACAzxJLHcl16NChctxxx4UfmzNnjmnF7NChQ0zvodOTPProoy2SSqWPXX311TJ8+PBEigcAAAAA8Hpi+e1vf7vFYz169Ig5qXTupVy2bJnp8hqJPqfzXgIAAAAAfJhY/uIXv4g4+tP//d//xfwe1157rVx22WXyzjvvyBlnnBFOIrdv3y6vvfaaPPTQQ3LnnXcmUjwkQAdd6t+/PxPZwiAeYCMeYCMe4EZMwEY8BFdCieUHH3wgkydPDv+9atWquCdCvfLKK6WkpETuuusu+e1vf2sG/1E6LPGJJ55ousnqSLPIDP3c6XoMB/EAG/EAG/EAN2ICNuIhuBJKLG+44YYWAXPNNdfE/T4XXHCB+amrqzNTjyhNNgsLCxMpFpKgib1eIDj++OOZcwjEA5ohHmAjHuBGTMBGPARXQollpKsQb775ZsKF0ETyyCOPTPj1SI2OHTtmuwjwEOIBNuIBNuIBbsQEbMRDMCWUWI4bN67ZaK7a2njWWWelslyyadMmmT59ujz88MMpfV9EpleUog2khOAhHmAjHmAjHuBGTMBGPARXQnfVnnPOOfLII4+YH70XcsmSJfLLX/4ypQXbuXOnPPbYYyl9T0RXX19v6lGXAPEAG/EAG/EAN2ICNuIhuBJqsdQ5Jt3effdd+eIXvxjze/zjH/9o9fl169YlUjQkKBQKSUVFhVkCxANsxANsxAPciAnYiIfgiimxvPzyy80AO9Fo4Hz44Yfy1ltvxfyPv/KVr5jutK0Fnd3dFgAAAACQw4llbW2tnH/++dK5c+eo6/zlL3+J6x/rYD06zci5554b8fmVK1eaaUcAAAAAAD5ILB944AEpKipqdZ2BAwfG9Y81aXznnXeiJpZttWYCAAAAAHJo8B53UqktmNddd5306dNHevXqJddee6307Nkzrn+srx85cmTU54855hiZN29eXO8JAAAAAMiRUWFvu+02+a//+i/54IMPZM2aNTJixAj5zW9+E9d7nH766TJp0qSoz2u32zFjxiRSPAAAAACA1xPL448/Xv77v/9bunXrJsXFxeZ35qvJ/TmHhg0bZpYA8QAb8QAb8QA3YgI24iG4EppupEOHDi0ey89PKEeFR2j9lZWVZbsY8AjiATbiATbiAW7EBGzEQ3AllFi+//770tjYKKNHj5aCggKZP3++eeycc86RbFu+fLnceuutsnXrVjOg0EMPPSSHHXaYPPvss9LQ0CAHDhyQU045RY477jiz/qZNm+Sll16Srl27mucuvfRSCSKdxHbhwoXhOkWwEQ+wEQ+xmfLLl2T77oNxvaZn1w7y4v87U3IJ8QA3YgI24iG44q5tTc7OPPNM+eMf/yjf/e53zeitOhXJHXfcIdmmgwo9//zz8txzz5nm9x/84Ady8803y+TJk81zF198sVnviiuukPvuu89cUdHnf/e735ntWLRokTz11FNy0UUXSdDoZzF06FBanmEQD7ARD7HRpPLtO74a12tO+ulfJdcQD3AjJmAjHoIr7sRSE7a7777btArq0kv+85//yFVXXRXu03322WfLgw8+KB9//HGzwYVKS0tNK2X//v1NwqlJpdJBiK6//vqoieXBgwfNj2P37t1mWVdXZ37cdIfSsmgyrvR3vYoTbRoVfV5fo+voUn8iva/DuQqk69u/R1NYWGhamvVH19elUzZ1+OGHm7/1Rz8TXccuu/6ur4nE2Va77LFsq25fLGW313G2wy67zSm7va2xlD1X6inStqajnjQeHNSTd+vJ3tZ01lNJSYlZT/8P9dR8W52yJ+JgbZ2cdEMsyaV+TzV9hj26dJC/Xz8hq/tTjx49TD1F+yy9XE9e2J/8eNyzzyHsbaWeHIlMoRdqsX25sj858eC8JnfqyZ/fT/lJ7E+tbXOLz0ASMGrUKJOsvfHGGyZwxo4d64mrEu7+3No994wzzjBJpU6L4tDftcvs9u3bmz2uFbxx48ZmAeEeDXfmzJktHp87d6506tSpxeOauA4fPlxWrVolHTt2NAMcLVu2TCoqKiKWX2901m3Q7gN6pUe/uPW9owXluHHjzPvOmTPHtMrW1NREnaJFt2fKlClSWVkpq1evlvHjx5tuwCtXroy4vibfOh3M2rVrzfs626GfTySDBw8Ob59OQ+Nsx549eyKuf+qpp4a3T0cIdrYjGmf7tFXZ2Y6lS5dGXLdLly7h7duyZUt4O3QE40iop+j1dPLJJ0vv3r2pp4DvT/qeL7/8shxxxBFy2mmnUU9R6ikRjZInb9/xtbheM/za2S3qN5P7k667YsUK83gu1lO29yeOe8Gsp4b6BpE4x7LR1+g2UU/sT6VZrKf9+/dLrPJC0VLXKHR1/Qc6HYhat26daSXU+xZnzJghXlFVVSU/+tGP5NFHHzWBp5XgtEw+/PDD5oMeMGCAedxOFjXo9Dk74WytxbJfv34mKPQezVy+MqL/55VXXpEJEyaY9bjS6M16iratqa4nJx50SiAdrIt68mY9ubc1XfWk/1e/YCZOnGi+bKin5tvqlP2U//ePuLvCHnf1bPngrvPjes1JP/2LLL35rKztT/oavdDgfF/kWj1le3/y43HPfQ6RrXrS+5x37DnoyR4AX7j5BVnTLr6LSIMb/iLv33RWzu1PdjwUFRWxP3m0nmI97mm+o72WqqurI+Y7SbVY6gZoxqstdNrN9L333jMD3nznO98Rr9APRbvpPvDAA6bSNAPXgXn0hEhpctizZ0/zuPuqhlaSfniR6Al2pBFx9X9E+3JV9nDLsdzEbK/T2vtGWqet9Z3gcv9uv95+D7vs+ntbQ0fbZY9lW+Mpu71OpLK72evEUvZcqie3dNWT83+pJ2/XUyJlt9dpq+zOF6lTBuopetkzIy/qNmdif3JOTNr67nPWifR7kPcn9zp+2p8ixUQm62nHntq4ewDofc6Z2Z+aGjeS2ddzbX/SMnIeUej5emqr7LFsc/h/SJx0A84991z58pe/bAbH0cFunJZAL9DE995775XrrrvOtFSqE0880TQVa7Ow2rZtm+nip62NekXFPnnSxzJ/kpBZY+6MNHJhyHS5+Ok7L7Q4+OmohQuuza1RCwEAAAB4fFRYncLjkksuES+6/fbb5bLLLgsnlc8884xMmzbN9Bm2E0vt+6xXBTQp1gF82rdvL0uWLDHdev1Ok8o17SJ014p20WN37o1aCAAAACBzEmqx/J//+R/xoscee0zuv/9+M2el0u46ekOuzmlZXl4ujz/+uOzatcskj05Ts06TolOPdO/e3XSXvfzyy7O8FQAAAPDTnLUH8urjHrwHyDUJ9fn0wgiwkWgrarSW1GiPa9fXa665Js0lAwAAQFDnrC376ey0lQfwCm9miACySu8z1mGz/X6/MWJDPMBGPMCNmICNeAguahxARM4oyoAiHmAjHuBGTGRywMXovDLgIvEQTCSWAFrQuYucCX7jGWYa/kQ8wEY8wI2YyMKAi+LdAReJh+AisQTQgnZf0S+EIHVjSWQwBr0y/OL/y/6V4XQLYjwgOuIBbsQEbMRDcFHjACKqqakJT9sTBIkMxqATawdF0OIBrSMe4EZMwEY8BBOJJYCI3VjmzZtHN5YAitxyGzJzGOt0UyJ5gW25RROOD3AjJmAjHoKLxBIAEEbLLQAguYGFQtJQ3yA/feeFFhcjvTK4ENKDxBKA7yQyih78K977Z2mBBZDLMjVmQKsDC2kHF48OLoT0IbEE4DuJjKJ3WDoLhJxqhaUFFgiOg7V1ctINf4vrNQfq6sXL6HmCbCGxBAAAQCA1Sl7cSdhxV89OW3mAXJaf7QIAAAAAAHIbLZZIyz1rihu0cxvzT6UH82UiW/cRp/J4zPEBbsQEcuX4hfThKID03LOmuEE7Z+nw4FOmTMl2MXyJe1+QrfuIU4XjA9yICeTK8QvpRWKJtCnYG/8N8bTMeENjY6NUVlZKSUmJ5OfTYx7AZzg+eE+2Rz4mJnJfldTJkJ/Hd85Wo4MYRRv9FYFEYglP3RCfSMsM3XRTf3KhJwmrV6+W0aNHc5KAtIyqyEWk3MXxwXuyPfIxMeGPc7Z4e6b1q2UQIzRHYomcRzfd1J9c6L0y48ePT2HJ4GeZuoiU6MUTpBbHB7gREwAUiSWAiFefN23aJP369ePqcw7Pf5bIAAlel6mWmUQGWfL63Hapi6GQhEIhycvLExH9aa62oV7atyvwbC+SROp2V3593IPT+LVnTCR8ZwBQJJZAmuViV92GhgZZuXKl9O7dO6snCYl+dpni9fnPEhkg4bB0Fsjngyz5cW67RHqEaPe49e3je01N1bNx39+V6HEykbot++ls+YSeMZ7/zgCQXSSWQMC76ka+eh8yJwo3L32hRYtEJu+N8/pnByA1QqH47+9iXw/eRU8A3kZiCfhoRF0vdy1M5CSGEecSH63P691aEx3wB8iVe25r6hLbbzOVuCVy4a5g67NR9ltvXIwEkF0kloAHk6NMDobi9S51SGy0Pq+35vgxxr3Ojxd3sj0aatBaYdlvAbSGxBK5PxhKXmInPokMbEJyBCBXcfwCkKsS6Z2wt33TcS8edPdODoklcv5qqA6qkKmBTeDflg8A8AIvd+8Fcql3giaWfus14HUklgDiRssHAASve28m7xGn1Qi5goGwPkNiCQAAAE/dI07LLXIFI9h/hsQSAAAAnuK3llsgCEgsAQAAAAReItO9JTqIpB+RWAIAAAAIvEwOIulH+dkuAAAAAAAgt5FYAgAAAACSQmIJAAAAAEgKiSUAAAAAICkklgAAAACApJBYAgAAAACSwnQjSQiFQma5e/duySUNB/fHdUmhoXF/Qpch9P/E+8kk8ppG3Z68OP9PAtuU6OeQUPkS/OzU7gy8piGUmc8hk3XbUBvf5+DXGPfy55DJGI/3Nc7rMvWaePfBTB6/Mrmvxxuvidat37Yp0eN4Jo9fmapbjuP+/I7OhRjfnSP5g1NOJ+9pTV4olrUQ0ebNm6Vfv37ZLgYAAAAApM2mTZukb9++ra5DYpmExsZG2bp1q3Tp0kXy8uK8VOHBqxGaJGvQdO3aNdvFQZYRD7ARD7ARD3AjJmAjHvxFU8U9e/ZI7969JT+/9SZgusImQT/ctjL3XKMHAA4CcBAPsBEPsBEPcCMmYCMe/KO4uDim9Ri8BwAAAACQFBJLAAAAAEBSSCxhdOjQQaZPn26WAPEAG/EAG/EAN2ICNuIhuBi8BwAAAACQFFosAQAAAABJIbEEAAAAACSFxBIAAAAAkBQSSwBAxjU2Nma7CAA8iuMDkJtILJFSjAWFaPbs2SMLFizIdjHggWOE/uTnN339bN++PdtFAuDR48O///1vkkwgh5BYIqXy8vLMctGiRfL3v/9d1qxZY/7miwEffPCBdO7cOdvFQBbpcUCPEfrz2muvyTHHHCO33367eY6LUsG2a9eubBcBHjs+fP7znzfHB00yOT4E244dO6S2tjbbxUAMSCyRMnrgr6mpkRtuuEH+v//v/5OFCxfKiBEjzBVHvhiCbefOnfLUU09JSUlJtouCLNLjwLp16+T888+XH/3oR3L11VfLgAEDpK6uLnxRCsGj3xUXXHCB6dWAYB8fNmzYIF/72tfM8eHKK6+UL37xixwfAu69996T7373u/Lhhx9muyiIAYklUkYP/HpF6aijjjJdHn/zm9/IpZdeKjfeeGP4efhbtIsHmkzoSYMmEQ0NDRkvF7xz1XnIkCFy9NFHmxZsPXHUVks9mYT/uXuuOMcLvSBZUVEhf/vb37JUMnhBVVWVjBs3To499tjw8UG/M5YvX57toiGLxwc9Nui55eLFi+nZkANILJG0zZs3y8GDB83vq1atkt69e4efmz59ummlcp6Hv7kvHjhfFB9//LFJKtT9998vDz30kIkVBOcYsX//funRo4dpnbrtttvCCYXeY0lLdjA49829++67sm/fPnO80O8GjYtf/epX8vjjj8uWLVuyXUxk2CeffGKOD927d5c///nPcvPNN5vH6+vrpbCwMHxOQa+nYBwfNIHUZNK5CF1aWiozZsyQd955R1avXk0ceFxBtguA3L66OG/ePHNyqAeEPn36yNixY2Xjxo0modDH3n//fRk6dKh06NAh28VFmunBXq8q3nPPPeaLQFurHcXFxebkYNOmTfKNb3xDnnnmGbniiivkiSeeMFek4f9jhCYRRx55pJxxxhnmOT1p6NixozmZnDNnjlx00UXZLi7STC8w/fCHPzRJhB4jpkyZIlOnTpXjjjtO2rdvb+7L14tOehKJYBwfXn/9dTlw4IA5FugtE5dddln4+FBQUGCWejFKvyfo9eRv2rNp2rRpUl1dLT179jQt17fccot87nOfM8cHjYO5c+fKwIEDzXcJvIkWSyScRKxYsUKGDRsm//M//yMnnXSS/OlPf5IHHnhA+vfvH76ipFcbhw8fbv7WLwhNOuFP+qWv3VS2bdsm8+fPNy2UzhVIPZF86623pF+/fqZ1Qk8uNU6efvpp8zxXIP1/jDj55JPNfbbaYq2cq9Ha9a1du3bmPir4l9b3q6++aro36q0SX/rSl+TNN980XR71pFH99Kc/Nc9piyaCcaHhC1/4gjk+fOUrXzHx8etf/7rZOmeeeabs3r3bJBvwt2XLlpkLC3oM+N73vmfiQy9MOscHvQil55DaNZqBfLyLxBIJJxE68uvWrVvN34MHDzYtUDfddJP5AtATRaVXIPXKk66vXw6acHCPnX9pIvFf//Vf5j7bhx9+OPz4eeedZ7q42Y/95Cc/Md2eFFeig3GM+MEPfmC6x+sxwjlZcC5A6EUo+Jd+J+jFhlGjRpm/J02aZJaHH364Wer3Qt++fU0r5oMPPpjVsiIztKdCeXm5+V1bJ3/+85/LfffdJ2vXrjXxojGh5w16EVJ7vcDf9KL0CSecYH4/8cQTzXeG9mhRGgvay0F7vOiIwdpVFt5EYom4OffN6SAc2tXVaW3SVsuJEyfKNddcY/7WK0pFRUUyaNAg8wWiLRfaFZIkwr8mTJggX//61+XLX/6yfPTRR7Jy5crwc3fffbfccccd4ZHd9ILD97///SyWFtk+RigdvEdPLhnAx9/0+2DkyJHSrVs387d2bTzssMPCLVFO74Zrr73W3Herg7/pMQS5PR9la8cHjYd//vOf4YvNetuMXljQ7o9Kk8uuXbuaezDp7eTfeHAeP+uss0yvJnXEEUfI3r17wz1ZnMYKbd3WY4VegHjjjTcyVn7EjsQSbXIfDJwTAF1qomgPAT1r1izzJeAkkLrjn3POOfK///u/8sc//lF+9rOfhV+P3BXtC8I5+OuVRr3iqPdQOs4++2zTleXJJ58009HoDfraDRK5Xf86wIZbrMcIh55gapdI+DMelLZQ60UEJ7HQ++r04pLeP+VWVlZmLkbqhUnkZjw481FqPUc7Pmhrta6rPZkcd955p7z99tum1VLpe2gvGH0M/owHp7HBSSqdCw16H6WeRzjv4dBWTL1orS2Y8B7O8BGRXiVyhn53tzA6O7ieDFZWVprkUa8sOV8UF154oXmNXmF85JFH5NxzzzX3Tug9NfBnTLhpHGjSqN2YXnrppfDj119/vekKqVcmdRAfvQkfuUfrX0f11AtFOt+cJgHOCYN9Zbq1Y4TdJf60006TyZMnZ2lrkO54cHTu3Dm8vtIujkpHhp09e3azbvLa3Y2BvXI7HvSeWR2Ua+nSpS2mklA67ZC2YOt9dDqQj+rUqZP5ntBWa4ceR3RuS/g7HpxBHp3jgzNAj6773HPPhdf7zne+Y84rdHAfeA+JJSLSq0E65LeOwqXc90Xqjq5XlzRB0ATSuVdO75PSdfXLQZOGNWvWmIMA/B0TkVow9aRBuzXpSI8aB7qOXqnWlosRI0ZktOxILR2kSQdX0LrUL3e9d1a7OtsnBW0dI5wukfB/PLiPD/rYv//9bzn++OPN33oB8oYbbjCPOS2WyF16oeDqq682FxK0e6tOLaXHfieZcC4+6TRDenzQY4IzqJfSdbUbPYITDzZ9Ts8ZPv/5z5u/daRo7QKrt1UoRoT1NqYbQQvasqBf8Hqf3F133SWjR48Od3FUTpcG7d6mXZv0qrKO5KZfEnq/jF6F1sRSDxYMyBGcmHCSTx02XrvCagxoTOgIb3qRQb9YdIAG+GNeSo0B5x5ZrV9tldRWKr3qHMsxgm6OwYoH5/ig9a5Jg8aG9mjQ1skuXbrIiy++SAuEjy40aE8lvbCo9N56HbxNLyZpcuHEgyYYGiOXX365+X54/vnn5dNPPzXxoN8jThdKBCMedPA/HUFee7Do/dV63/2NN95o1tOu0DotEbyPszyYnV7vedKb5HWgHT2g69QA2lVR74nTq0V6NVpbGTSZ0FEef/vb35pWCB0FVq9AabKhXd640hjsmNATTL1/0qFfCDrpuQ7agtyPBz3h05N/nZNUv+Sd+tcLBnoS6CSLHCP8LdF40OODnigq7eqqiYXGh7ZawT/xoPfAaWu0xoC2XutclTqxvd5X7UxFde+995q/dbCmXr16mfjYs2eP6TIZ6b5b+D8e9PigF5qUTk+mF5t++ctfhhNS5Ia8EBPIBZrOF6YtUbrz60Arp59+urmfQQdVUNrtcebMmfKXv/wlPNy3XlXUaUQuueSSLJce6UBMoLV4GDt2rGmRshMJTRz0Pupvfetb5jXEg3+lKh50Dku6xPsvHrTlWutVk0U9vdSWJ00UtXVap5rReUw1ZrQF6pvf/Ga2iw+PxoN2hdWeT8g9JJYB9+yzz5qdXCcp1lEZ9QZp7cKoAzDo/TLaB15vuNYurXrlyE2fZ5RXfyEm0Fo86AUFbcHWKUOcen7hhRdM67aeROgJgz7vdIMnHvwl2XjQUWPpEh+c7wu9oKADdml3R70t4tZbb5W5c+eGX8/xwV+SjQeOD7mPvTngN1Tr/U46l5jSK856Benjjz82JwtKD/ja5VGvQmnXBodzPYIvBH8hJtBWPOj9LxoPegLh0C5NmkQo7fKqP8oZsAn+kIp44KQxWN8XThLhDMqko7tqHDgDt3B88I9UxAPHh9zHHu1juqPq1R+9rynSyK46qMKyZcvk5ZdfDj+m80XplSbthqBXmpV2adCDw1e/+lVzc7XihvrcREwglfGgV5+Vjt6n0wVcdtllZkoaZxoJYiK3EA9IZTzoPZP2KNHagtWjRw/zGAll7iEeEAtq0ofsliO9UnTdddeZv+1RPJ2rQzoViM4rqHMUKh2hS68yOdNC6HvpiKB6E7YO9zx8+PCsbBOSQ0wgHfGgV5/1JEO7Oem8pXoFWi806CBPyB3EA9IRD9r9uaamRn7+85+bLo99+/ZlPsocRDwgHiSWPuRcEXryySfNF7peVdIhve3baXUn1ytPelP1qaeeaob6duiN1NqVSQ8A+l56YLjiiivCBxPkHmIC6YgH7fakI/qdf/755ir1z372s6xsD5JDPCAd8aCv0ftrdbohnVLkhBNOyMr2IDnEA+Kig/cg9zU2Njb7+69//WvoxBNPDL3xxhsR121oaAj/XVVVFRozZkzo+eefN7+rxx57LLRv374MlBzpQkwgnfHwyCOPhA4ePNhsPeQO4gHpjIdHH32U74scRjwgUYwKm+PsCYSdod6VXhnSOYDOO+88cxX5H//4h5lHyL5CpPfLzZo1ywzxrOs7E9LqtBI6z9yJJ56Yte1C4ogJ2IgH2IgH2IgH2IgHJC3hlBSecs8994S++c1vhmbPnh0qLy83j73zzjuhH/zgB6EePXqELrrootCxxx4buvvuu0Pr1q0zz33ta18L3Xvvvc2uNK1fv77FlSrkJmICNuIBNuIBNuIBNuIBiaLFMsdpn/abbrrJXEH64he/KIsWLTIjc/3+9783z+sw7+PHjzdXlVauXGl+9CqUjtRVUlJiJq1VzCXlH8QEbMQDbMQDbMQDbMQDksWEMTlu165dZiLq2267zfytO/uf/vQn0/1AR+T7wQ9+IEVFRWbHHzZsmGzfvt28ZujQoWZ95h70H2ICNuIBNuIBNuIBNuIByaLmc/Bq0ttvv23mBFJ6hUh37traWvN3cXGxmStIDwBKDwDK6Sevwzvbcw9pX3rmFsttxARsxANsxANsxANsxANSjRbLHLiJ2ulSsG7dOnMA6Nevnzz//PPy2muvyejRo023BGd+QZ24XiejdTt48KAsWbLEzEGkQz8jNxETsBEPsBEPsBEPsBEPyARaLD1Kd3znqo+z3Lx5sxx22GFmjqDLLrvMXEX6y1/+IlVVVeH11q5dK0cccYT5W+cc1FG69u3bJ9dcc43Mnj1bzj33XBk0aFAWtwyJIiZgIx5gIx5gIx5gIx6QKQze42Hl5eXy61//2vR31ytC2jWhe/fuMnLkSPP8qlWr5IknnjA7/Q033GAe04OCHiSOPPJI+dvf/iYvvPCC/OEPf5D//Oc/5jHkNmICNuIBNuIBNuIBNuIBmUCLpUd98skncuWVV5q5fzp16mRunr799tvNPEIOnUPo5JNPNleXnP7xepDQg4KO6nXzzTeb/u+KA0DuIyZgIx5gIx5gIx5gIx6QKdxj6VHr1683o2+dddZZptvBX//6V9Mf/t5775Wzzz5bRowYER6xS68yde7c2fytI3npa7/0pS/J3LlzzUEB/kBMwEY8wEY8wEY8wEY8IFNILD2qf//+cuDAAdMvXnfwnTt3ykknnWR28mnTpsmbb75p1tMbq3VELu2W0KVLFxk1apS5qqRXneAvxARsxANsxANsxANsxAMyhXssPerTTz81/eD15mkdvUsnqdUDwpgxY8zVJb1hWkfuOvroo+Wpp56SM844Q3r27JntYiONiAnYiAfYiAfYiAfYiAdkCi2WHnX44Yc3+3v16tVmp1fa111H5nruueekV69epq87BwD/IyZgIx5gIx5gIx5gIx6QKSSWHqdXlLRRWbsmDB482Dz2wAMPyDe/+U1zI7Y+5/SFRzAQE7ARD7ARD7ARD7ARD0g3RoX1OGcyWx3uWUfpOv/8882VJu3SoCN7cQAIHmICNuIBNuIBNuIBNuIB6UaLZQ4cBFauXCkXXnihGfL5+9//vnz729/OdrGQRcQEbMQDbMQDbMQDbMQD0o3EMgfU1dXJZZddJj/+8Y+lQ4cO2S4OPICYgI14gI14gI14gI14QDoxKmwO0CrSq0yAg5iAjXiAjXiAjXiAjXhAOpFYAgAAAACSwuA9AAAAAICkkFgCAAAAAJJCYgkAAAAASAqJJQAAAAAgKSSWAAAAAICkkFgCAAAAAJJCYgkAAAAASAqJJQAAAAAgKSSWAAAAAICkkFgCAAAAAJJCYgkAAAAAkGT8/yqcL3uB9az1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1150x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制带成交量的K线图\n",
    "# 使用mplfinance模块\n",
    "import mplfinance as mpf\n",
    "\n",
    "# 读取数据\n",
    "file_name = './000001(20250101-20250328).csv'\n",
    "df = pd.read_csv(file_name).rename(columns={'Unnamed: 0':'date'})\n",
    "\n",
    "# 时间格式转化,并生成年，月列\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['year'] = df['date'].dt.year\n",
    "df['month'] = df['date'].dt.month\n",
    "df_mpf = df.set_index('date')\n",
    "\n",
    "# 设置颜色\n",
    "my_color = mpf.make_marketcolors(\n",
    "    up=\"red\",  # 上涨K线的颜色\n",
    "    down=\"green\",  # 下跌K线的颜色\n",
    "    edge=\"i\",  # 蜡烛图箱体的颜色\n",
    "    volume={'up': 'red', 'down': 'green'},  # 成交量柱子的颜色\n",
    "    wick=\"i\", # 蜡烛图影线的颜色\n",
    "    ohlc = 'i',\n",
    ")\n",
    "\n",
    "# 设置样式\n",
    "my_style = mpf.make_mpf_style(\n",
    "                            # base_mpl_style=\"ggplot\", \n",
    "                              marketcolors=my_color,\n",
    "                             gridaxis='both',\n",
    "                             gridstyle='-.',\n",
    "                             rc = {'font.family':'STSong'})\n",
    "\n",
    "# 画图\n",
    "mpf.plot(\n",
    "        df_mpf,\n",
    "        type='candle',\n",
    "        title='K-LineByVolume',\n",
    "        ylabel='price',\n",
    "        style=my_style,\n",
    "        volume=True,\n",
    "        ylabel_lower = 'volume',\n",
    "        datetime_format='%Y-%m-%d',\n",
    "        xrotation=30,\n",
    "        figratio=(12, 6),\n",
    "        linecolor = '#00ff00',\n",
    "        tight_layout=False,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "333456df-8234-43b4-ba01-87c4964f707a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1150x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制K线图带均线和成交量量\n",
    "\n",
    "import mplfinance as mpf\n",
    "\n",
    "# 读取数据\n",
    "file_name = './000001(20250101-20250328).csv'\n",
    "df = pd.read_csv(file_name).rename(columns={'Unnamed: 0':'date'})\n",
    "\n",
    "# 时间格式转化,并生成年，月列\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['year'] = df['date'].dt.year\n",
    "df['month'] = df['date'].dt.month\n",
    "df_mpf = df.set_index('date')\n",
    "\n",
    "# 设置颜色\n",
    "my_color = mpf.make_marketcolors(\n",
    "    up=\"red\",  # 上涨K线的颜色\n",
    "    down=\"green\",  # 下跌K线的颜色\n",
    "    edge=\"i\",  # 蜡烛图箱体的颜色\n",
    "    volume={'up': 'red', 'down': 'green'},  # 成交量柱子的颜色\n",
    "    wick=\"i\", # 蜡烛图影线的颜色\n",
    "    ohlc = 'i',\n",
    ")\n",
    "\n",
    "# 设置样式\n",
    "my_style = mpf.make_mpf_style(\n",
    "                            # base_mpl_style=\"ggplot\", \n",
    "                              marketcolors=my_color,\n",
    "                             gridaxis='both',\n",
    "                             gridstyle='-.',\n",
    "                             rc = {'font.family':'STSong'},\n",
    ")\n",
    "\n",
    "mpf.plot(\n",
    "    df_mpf,\n",
    "    type = 'candle',\n",
    "    mav = [5,10,20], # 生成5日、10日、20日均线\n",
    "    title='K-LineByVolume',\n",
    "    ylabel='price',\n",
    "    style=my_style,\n",
    "    volume=True,\n",
    "    ylabel_lower = 'volume',\n",
    "    datetime_format='%Y-%m-%d',\n",
    "    xrotation=30,\n",
    "    figratio=(12, 6),\n",
    "    linecolor = '#00ff00',\n",
    "    tight_layout=False,\n",
    ")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
